Moving Element Monitoring

Abstract

A system for monitoring a moving element includes a processing circuit that receives image data of one or more sections of the moving element from at least one optical sensor. For each of these sections, a range of motion of the moving element in a secondary direction of motion is determined from the image data. The range of motion is analyzed to determine the health of the moving element, and an indicator is output along with the analysis result. The indicator may trigger an action in other system components, such as a system controller.

Description

[Technical Field]

[0001] The present disclosure, in some embodiments thereof, relates to monitoring of moving elements, and more particularly, but not exclusively, to monitoring of high speed moving elements, such as loop or rotary moving elements. [Background technology]

[0002] Machinery maintenance is important in many fields, including manufacturing, aviation, vehicles, and many others. An effective maintenance strategy can help prevent breakdowns and enable organizations to stick to production plans, minimize costly downtime, and lower the risk of accidents and injuries.

[0003] Machine maintenance may include any activity that maintains a mechanical asset in operation with minimal downtime to the machine and / or component. Maintenance may also include replacing or reconditioning worn, damaged, or misaligned parts.

[0004] Currently, industrial maintenance is typically scheduled for set periods of time (routine maintenance), perhaps based on factors such as statistical and / or historical data and / or usage levels (e.g., mileage or hours of operation). Maintenance may also be performed upon failure of a machine, part, or component (reactive maintenance). This type of maintenance is often wasteful and inefficient.

[0005] In some cases, predictive maintenance is also based on signals or other data provided by vibration sensors, but this data typically does not indicate the cause of unexpected vibrations and is therefore less useful in establishing maintenance protocols.

[0006] Moving and / or rotating elements within a machine or system are very common and, in many instances, are critical to the proper operation of the machine or system. Often, manual inspection and monitoring of these elements is performed by technicians or other maintenance personnel. However, in some instances, it is critical that elements be monitored automatically and in real time to ensure the machine or system functions properly and to prevent machine or system failure.

[0007] For example, in the case of a pulley belt, monitoring the tension in the belt is typically performed manually while the belt is stationary (i.e., not moving), e.g., using a pen gauge pressed against the belt and / or using an acoustic stretch meter that measures the belt tension according to the belt's self-frequency. However, in some machines and vehicles, such as unmanned aerial vehicles (UAVs), real-time monitoring of timing belts is crucial to prevent belt release, which could cause the UAV to malfunction.

[0008] Therefore, there is a need in the art to provide a system and method for monitoring moving elements in real time. Summary of the Invention [Means for solving the problem]

[0009] According to some embodiments, a system, method, and computer program product are provided for monitoring moving elements.

[0010] Some embodiments of the invention presented herein perform image analysis to monitor a moving element having a primary direction of movement. Image data is provided by one or more optical sensors that capture image data for each section of the moving element. In some embodiments, the image data is analyzed to determine the extent of movement of the moving element in a secondary direction.

[0011] Optionally, the moving element is a loop moving element moving around a pulley. Alternatively, the moving element is a rotary moving element rotating around an axis.

[0012] Examples of loop and rotary moving elements and their respective primary and secondary directions of movement are provided below.

[0013] As used herein, the terms "primary direction of movement" and "primary direction" refer to the way in which a movable element moves to perform its function.

[0014] As used herein, the terms "secondary direction of movement" and "secondary direction" refer to the direction in which a movable element moves, but not necessarily to perform its function.

[0015] In some embodiments of the present invention, image data is used to identify possible problems with the physical aspects of the moving element, for example, its material composition, structure, shape, etc.

[0016] For example, a change in the thickness of the loop moving element may indicate wear of the loop moving element. In another example, an uneven surface of the loop moving element may indicate a tear or protrusion. In a further example, a change in color of the loop moving element may indicate degradation of the material forming the loop moving element.

[0017] In other examples, the image data may be used to detect differences between the shape of the rotatable moving element and a previous or desired shape.

[0018] Some embodiments of the present invention may analyze image data to monitor failure modes of additional components (i.e., other than moving elements), thereby expanding the capabilities of the monitoring systems and methods described herein.

[0019] Optionally, the image data additionally includes data from optical sensors viewing other elements, which may be used to analyze health and / or other parameters associated with the associated component and / or external factors (faults / failures in the associated component, whether a system including moving elements is moving or fixed, operating conditions, etc.).

[0020] The range of motion in the secondary direction(s) and other factors that can be determined therefrom can be used to monitor and analyze the operation and / or health of the moving element. If a problem is detected, the operation of the moving element and / or the system of which it is part can be controlled accordingly to alleviate the detected problem. The results of the analysis can also be used for preventive maintenance purposes, for example, to expedite maintenance through early detection of problems that might otherwise only be detected later, and / or to prevent unnecessary maintenance if the moving element is in good health.

[0021] One aspect of an embodiment of the present invention relates to monitoring failure modes associated with tension in a looped moving element. Maintaining the correct tension in a looped moving element can be critical to ensuring a machine or system is operating correctly. For example, if the range of motion in a secondary direction is greater than a specified threshold, the looped moving element may be operating below the required tension, indicating a fault or failure (e.g., belt distraction, belt in-pulley movement, etc.).

[0022] An alternative or additional aspect of an embodiment of the present invention relates to monitoring failure modes associated with defects in the loop moving element, for example, a fray or tear in the loop moving element may result in a protrusion or bump on the surface of the loop moving element, which may be detected by analyzing images of a section of the loop moving element.

[0023] A further alternative or additional aspect of an embodiment of the present invention relates to monitoring failure modes associated with changes in the shape of a moving element, for example, a change in the shape of a rotatable moving element may indicate deformation of the rotatable moving element or breakage of the rotatable moving element.

[0024] A still further alternative or additional aspect of an embodiment of the present invention relates to monitoring failure modes associated with changes in the appearance of a moving element. For example, changes in color, brightness, or blurred appearance of a moving element may indicate changes in the speed, frequency, acceleration, and deceleration of the moving element's movement in the primary and / or secondary directions.

[0025] Optionally, information about the health of the moving elements is provided to predictive maintenance systems, such as Prognostic Health Management (PHM), Condition Based Maintenance (CBM) and Health Status and Usage Monitoring Systems (HUMS).

[0026] Accurately monitoring the range of motion of a moving element in a secondary direction(s) over time may allow for faults to be identified and / or predicted before they become serious, so that their occurrence can be avoided through predictive maintenance.

[0027] Some embodiments of the present invention provide a technical solution to the technical problem of preventing operation of an associated system when there is a failure in a moving element. If the analysis identifies a fault in the moving element, an indicator may trigger a control action such as stopping operation of the associated system and / or otherwise changing an operating parameter (e.g., rotation speed).

[0028] Some embodiments of the present invention provide a technical solution to the technical problem of detecting faults early to eliminate system failures. The technical solution may trigger measures, such as early maintenance, as soon as a fault is detected or even suspected. Early maintenance may prevent future failures.

[0029] Some embodiments of the present invention provide a technical solution to the technical problem of monitoring moving elements in locations with severe space constraints. The technical solution can be to use a single small optical sensor positioned close to the moving element. For example, an optical sensor placed 2 cm from the moving element can capture images of a 1 cm section of the moving element, which is sufficient to determine the range of movement and identify faults.

[0030] Some embodiments of the present invention provide a technical solution to the technical problem of monitoring fast moving elements using optical sensors. The technical solution may be to use simple optical sensors with low-average frame rates to enable analysis of blurred images of fast moving elements. Embodiments of the present invention relate to the analysis of blurred images to receive indicators of the health of the imaged components and their associated elements.

[0031] The advantages of the present invention may include, but are not limited to: 1) Early detection of faults and failures; 2) Rapid detection of critical faults; 3) real-time control of moving or related elements in response to detected faults and / or failures; 4) preventative and predictive maintenance can be based on the progression of travel range over time; 5) Suitable for monitoring many types of systems and equipment, including manufacturing machinery, vehicles, aircraft and many more. 6) It can be used in a wide range of environmental conditions. 7) Allowing for monitoring of moving elements by placing optical sensor(s) within an otherwise inaccessible area and within view of a moving element or part that may not otherwise be monitored.

[0032] According to a first aspect of some embodiments of the present invention there is provided a system for monitoring a moving element including a processing circuit, the processing circuit comprising: inputting image data of at least one segment of the movable element from at least one optical sensor; determining, from the image data, a range of movement of at least one segment of the movable element in a secondary direction of movement relative to a primary direction of movement of the movable element; and outputting an indicator of the health of the moving element based on the analysis of the range of movement; The device is configured to:

[0033] According to a second aspect of some embodiments of the present invention, there is provided a method for monitoring a moving element, the method comprising: inputting image data of at least one segment of the movable element from at least one optical sensor; determining, from the image data, a range of movement of at least one segment of the movable element in a secondary direction of movement relative to a primary direction of movement of the movable element; and outputting an indicator of the health of the moving element based on the analysis of the range of movement; Includes:

[0034] According to a third aspect of some embodiments of the present invention there is provided a non-transitory storage medium storing program instructions which, when executed by a processor, cause the processor to perform the method of the second aspect and any embodiments thereof.

[0035] According to some embodiments of the invention, the movable element is a rotationally movable element, and the primary direction of movement is rotational movement and the secondary direction of movement is linear movement.

[0036] According to some embodiments of the invention, the movable element is a loop movable element, and the primary direction of movement is a first longitudinal movement of the loop movable element, and the secondary direction of movement is transverse to the first longitudinal movement.

[0037] According to some embodiments of the present invention, the transverse movement is a second longitudinal movement perpendicular to the first longitudinal movement.

[0038] According to some embodiments of the invention, the loop-shaped movable element is one of: Pulley belt; cable; strap; rope; and chain.

[0039] According to some embodiments of the present invention, operation of the moving element is controlled based on the health of the moving element to prevent operation of the moving element during a fault.

[0040] According to some embodiments of the present invention, the indicator is output to a controller configured to control operation of the moving element based on the health of the moving element to prevent operation of the moving element during the fault.

[0041] According to some embodiments of the present invention, the indicator is output to a preventive maintenance system configured to provide maintenance instructions based on the indicator.

[0042] According to some embodiments of the present invention, an indicator is displayed on a user interface to alert the user to the health of the moving components.

[0043] According to some embodiments of the invention, the indicator includes at least one of the following: maintenance instructions; time-to-failure estimation; Fault alerts; and Operational instructions in response to detected faults.

[0044] According to some embodiments of the present invention, the moving element is a loop moving element, and in terms of range of motion analysis, a larger range of motion indicates a lower tension in the moving element compared to the tension at a smaller range of motion.

[0045] According to some embodiments of the present invention, the analysis includes assessing the health of the moving element by comparing the magnitude of the range of motion to at least one threshold value.

[0046] According to some embodiments of the present invention, the range of motion is determined by calculating the maximum amplitude of the contour of the moving element in an image captured with an exposure time greater than the time expected for the moving element to move through the entire range of motion.

[0047] According to some embodiments of the present invention, the exposure time is selected to blur moving elements in the image data.

[0048] According to some embodiments of the present invention, the optical sensor is controlled to capture images with an exposure time that exceeds the time expected for the movable element to move through its entire range of motion.

[0049] According to some embodiments of the present invention, the range of motion is determined based on a statistical analysis of a series of image frames.

[0050] According to some embodiments of the present invention, the image data is a video sequence of images.

[0051] According to some embodiments of the present invention, the health of a moving element is assessed based on changes in the magnitude of its range of motion over time.

[0052] According to some embodiments of the present invention, the health of the moving element is assessed based on the shape of at least one segment.

[0053] According to some embodiments of the present invention, the health of the moving element is assessed based on changes in the shape of the moving element in different sections of the moving element.

[0054] According to some embodiments of the present invention, image data is input from a plurality of optical sensors, each optical sensor capturing image data for a respective section of the moving element.

[0055] According to some embodiments of the present invention, image data is input from a single optical sensor that is in a fixed position relative to the moving element.

[0056] According to some embodiments of the present invention, at least one section of the movable element comprises at least 10% of the length of the movable element.

[0057] According to some embodiments of the present invention, the indication is obtained from a data structure indexed, at least in part, by at least one parameter determinable from the movement range.

[0058] According to some embodiments of the present invention, the analysis is based on a machine learning model trained using a training set of images collected during operation of at least one of the moving element and similar moving elements.

[0059] According to some embodiments of the present invention, the machine learning model is a neural network.

[0060] According to some embodiments of the present invention, training of the machine learning model is performed using a supervised learning algorithm.

[0061] According to some embodiments of the present invention, training of the machine learning model is performed using an unsupervised learning algorithm.

[0062] According to some embodiments of the present invention, the analysis includes predicting the future health of the moving element by performing a trend analysis on changes in range of motion over time.

[0063] According to fourth and fifth aspects of some embodiments of the present invention, there are provided systems and methods, respectively, for monitoring the status and / or integrity of operation of a movable and / or rotating element, optionally fixed at at least one end thereof to a stationary element with respect to the element's movement and / or rotation. Optionally, the monitoring is performed automatically and / or in real time and / or while the element is in motion. According to sixth and seventh aspects of embodiments of the present invention, there are provided systems and methods, respectively, for predictive maintenance of an element.

[0064] According to some embodiments of the present invention, the system optionally includes at least one optical sensor, such as a camera, configured to be fixed on, adjacent to, or within view of the movable and / or rotating element, optionally fixed at at least one end thereof to an element stationary with respect to the element's movement and / or rotation, and at least one processor in communication with the at least one optical sensor. The system may be configured to provide an indication of the status and / or integrity of the element's operation. Optionally, the use of the at least one optical sensor allows the status and / or operation of the movable and / or rotating element to be monitored automatically and in real time during operation.

[0065] In some embodiments of the present invention, the movable and / or rotating element is a loop movable element as described herein.

[0066] In an alternative embodiment of the present invention, the movable and / or rotating element is a rotary movable element as described herein.

[0067] According to some embodiments of the present invention, there is presented a system for monitoring the status and / or integrity of operation of a movable and / or rotating element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the element, the system comprising: at least one optical sensor configured to be fixed on, in proximity to, and / or within view of the element, which is configured to capture multiple images of the element while in motion; 1. A processor, comprising: receiving a plurality of images captured from at least one optical sensor; Calculating the maximum amplitude of the secondary movement of the element and / or the displacement of the element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the element and / or combinations thereof; and outputting a signal indicating a fault in or associated with the element if the maximum amplitude of the secondary movement and / or displacement of the element exceeds a predefined threshold; a processor capable of executing Includes:

[0068] According to some embodiments of the present invention, the processor: measuring the contour, perimeter and structure of the imprint formed along the movement of the element in each of the plurality of images; and Calculating the maximum deviation of the signature from a baseline of known signatures when operating properly; By doing the following, It may be feasible to calculate the maximum amplitude of the secondary movement of the element and / or the displacement of the element along its axial and radial movements.

[0069] According to some embodiments of the present invention, the system further includes an illumination source configured to illuminate the element with light pulses during capture of the plurality of images, wherein the pulse duration is shorter than a shutter exposure time of the at least one sensor, and the processor is executable to calculate a maximum amplitude of secondary movement of the element and / or a displacement of the element along an axis perpendicular to the axis of motion and / or axial and radial movement of the element and / or combinations thereof.

[0070] According to some embodiments of the invention, monitoring the status and / or integrity of operation of the movable and / or rotating elements includes monitoring at least one member of the following list: ·tension; · Tightness; ·Completeness; ·concentration; ·Straightness; ·Stability; Dynamic balance; · Symmetry; ·frequency; ·rigidity; · Perpendicularity (e.g., orthogonality or right-angled arrangement); · Centrality (e.g., centering or symmetry); · Cylindricity (e.g., roundness or roundness); · Dimensional stability (e.g., size consistency or geometric stability); Thermal expansion (e.g., coefficient of thermal expansion (CTE) or thermal expansion): · vibrations (e.g. mechanical or dynamic vibrations); · Alignment (e.g., geometric alignment or positioning); deformation (e.g., distortion or geometric change); and / or Alignment of moving and / or rotating elements.

[0071] According to some embodiments of the invention, the element is fixed at its two ends.

[0072] According to some embodiments of the invention, the element is a belt connected between two pulleys.

[0073] According to some embodiments of the present invention, the belt is a toothed belt.

[0074] According to some embodiments of the present invention, the belt is a timing belt.

[0075] According to some embodiments of the present invention, the belt is a flat belt.

[0076] According to some embodiments of the invention, the element is a shaft.

[0077] According to some embodiments of the invention, the element is a rotor, a propeller, a turbo, a fan blade, an impeller, a turbine blade and / or a turbo blade.

[0078] According to some embodiments of the invention, the predefined threshold is based on the maximum amplitude of the secondary movement of the element and / or the displacement of the element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the element and / or combinations thereof, calculated under normal operation.

[0079] According to some embodiments of the present invention, the processor: applying a fault detection algorithm to the plurality of images to detect potential faults in the element or a segment thereof based on predefined fault detection parameters; outputting a signal indicating a detected fault; It is further feasible to do the following:

[0080] According to some embodiments of the present invention, the fault detection algorithm: obtaining data associated with fault detection parameters for at least one failure mode of the element; and identifying at least one change in at least one image of the plurality of images compared to a given image of the element in an appropriate context or compared to a previously acquired image of the element; for the identified variations, applying at least one identified variation to an algorithm configured to analyze the identified variations and classify whether the identified variations are associated with a failure mode of the element, thereby labeling the identified variations as detected faults based, at least in part, on the acquired data; and for an identified change classified as being associated with a failure mode, outputting a signal indicative of the identified change associated with the failure mode; The device is configured to:

[0081] According to some embodiments of the present invention, for a detected fault, at least one model of trends in the identified faults is generated.

[0082] According to some embodiments of the invention, the trend comprises a rate of change in the disorder.

[0083] According to some embodiments of the present invention, generating at least one model of a trend in the detected disorder includes calculating a correlation between a rate of change of the disorder and one or more environmental parameters.

[0084] According to some embodiments of the present invention, the processor is further configured to alert a user of a predicted failure based at least in part on the generated model.

[0085] According to some embodiments of the present invention, alerting the user of a predicted failure includes any one or more of the time (or time range) of the predicted failure, the age of the element and characteristics of the mode of failure, or any combination thereof.

[0086] According to some embodiments of the present invention, the processor may be further configured to output a prediction of when the detected fault is likely to lead to a failure in the element based at least in part on the generated model.

[0087] According to some embodiments of the present invention, predictions of when failure is likely to occur in an element are based at least in part on known future environmental parameters.

[0088] According to some embodiments of the present invention, obtaining data associated with fault detection parameters of at least one failure mode of the element includes data associated with a location of the fault on the element and / or a particular type of failure mode.

[0089] According to some embodiments of the present invention, obtaining data associated with fault detection parameters of at least one failure mode of the element includes receiving data input from a user.

[0090] According to some embodiments of the present invention, obtaining data associated with fault detection parameters for at least one failure mode of the element includes identifying a previously unknown failure mode by applying the plurality of images, or a portion thereof, to a machine learning algorithm configured to determine a failure mode of the element.

[0091] According to some embodiments of the present invention, there is presented a method for monitoring the status and / or integrity of operation of a movable and / or rotating element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the component, the method comprising: capturing multiple images of the element while in motion using at least one optical sensor; receiving, by a processor, a plurality of images captured from the at least one optical sensor; Calculating the maximum amplitude of the secondary movement of the element and / or the displacement of the element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the element and / or combinations thereof; and outputting a signal indicative of a fault in or associated with the element if the maximum amplitude of the secondary movement of the element and / or the displacement of the element exceeds a predefined threshold; The method includes the step of:

[0092] According to some embodiments of the present invention, calculating the maximum amplitude of the secondary movement of the element and / or the displacement of the element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the element comprises: applying a trace detection algorithm to each of the plurality of images to measure the contour, perimeter, and structure of a trace formed along the movement of the element; and Calculating the maximum deviation of the signature from a baseline of known signatures when operating properly; The method includes the step of:

[0093] According to some embodiments of the present invention, the method comprises: illuminating the element with light pulses by an illumination source during capture of the plurality of images, the pulse duration being less than a shutter exposure time of at least one sensor; Calculating the maximum amplitude of the secondary movement of the element and / or the displacement of the element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the element and / or combinations thereof; The method further includes the step of:

[0094] According to some embodiments of the invention, monitoring the status and / or operation of the movable and / or rotating elements includes monitoring at least one member of the following list: ·tension; ·Upholstery; ·Completeness; ·concentration; ·Straightness; ·Stability; Dynamic balance; · Symmetry; ·frequency; rigidity; and / or Alignment of moving and / or rotating elements.

[0095] According to some embodiments of the present invention, the method comprises: applying a set of fault detection algorithms to the plurality of images to detect potential faults in the element or a section thereof based on predefined fault detection parameters; and outputting a signal indicative of a detected fault; Further includes:

[0096] According to some embodiments of the present invention, the fault detection algorithm: obtaining data associated with fault detection parameters for at least one failure mode of the element; and identifying at least one change in at least one image of the plurality of images compared to a given image of the element in an appropriate context or compared to a previously acquired image of the element; for the identified variations, applying at least one identified variation to an algorithm configured to analyze the identified variations and classify whether the identified variations are associated with a failure mode of the element, thereby labeling the identified variations as detected faults based, at least in part, on the acquired data; and for an identified change classified as being associated with a failure mode, outputting a signal indicative of the identified change associated with the failure mode; The device is configured to:

[0097] According to some embodiments of the invention, features of the first, second, third, sixth and seventh aspects of the invention may be combined, for example an optical sensor, illumination means and processor described in conjunction with any of the aspects may be provided in conjunction with any other aspect.

[0098] Unless otherwise defined, all technical and / or scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this disclosure pertains. Methods and / or materials similar or equivalent to those described herein can be used in the practice and / or testing of embodiments of the present disclosure, and exemplary methods and / or materials are described below. With regard to the exemplary embodiments described below, the materials, methods, and examples are illustrative and not necessarily intended to be limiting.

[0099] Some embodiments of the present disclosure are embodied as a system, method, or computer program product. For example, some embodiments of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which may be referred to generally herein as a "circuit," "module," and / or "system."

[0100] Implementation of some embodiment methods and / or systems of the present disclosure may involve performing and / or completing selected tasks manually, automatically, or a combination thereof. Depending on the actual instrumentation and / or apparatus of some embodiment methods and / or systems of the present disclosure, some selected tasks may be implemented by hardware, software, or firmware, and / or a combination thereof, for example, using an operating system.

[0101] For example, hardware for performing selected tasks according to some embodiments of the present disclosure may be implemented as a chip or circuit. As software, selected tasks according to some embodiments of the present disclosure may be implemented as a plurality of software instructions executed by a computing device using, for example, any suitable operating system.

[0102] In some embodiments, one or more tasks according to some exemplary embodiments of the methods and / or systems as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes volatile memory for storing instructions and / or data and / or non-volatile storage, e.g., for storing instructions and / or data. Optionally, a network connection is also provided. User interface(s), e.g., display(s) and / or user input device(s), are optionally provided.

[0103] Some embodiments of the present disclosure may be described below with reference to flowcharts and / or block diagrams. For example, exemplary methods and / or apparatuses (systems) and / or computer program products according to embodiments of the present disclosure are illustrated. It will be understood that each step of the flowcharts and / or blocks of the block diagrams, and / or combinations of steps in the flowcharts and / or blocks in the block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to create a machine, such that the instructions, executing via the processor of the computer or other programmable data processing device, create means for implementing the function / act specified in the flowchart step and / or block diagram block or blocks.

[0104] These computer program instructions may also be stored in a computer-readable medium that can direct a computer (e.g., in memory, locally and / or cloud-hosted), other programmable data processing apparatus, or other device to function in a particular manner, such that the instructions stored in the computer-readable medium can be used to generate an article of manufacture including instructions that implement the function(s) / act(s) specified in the flowchart and / or block diagram block or blocks.

[0105] The computer program instructions may also be executed by one or more computing devices to cause a series of operational steps to be performed, for example, on the computing device, other programmable device, and / or other device to generate a computer-implemented process, such that the executing instructions provide a process for implementing the function / act specified in the flowchart and / or block diagram block or blocks.

[0106] In order to understand the present invention, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which features shown in the drawings are intended to serve to illustrate some embodiments of the invention only, unless otherwise indicated, and in which like reference numerals are used to indicate corresponding parts.

[0107] In the block diagrams and flow diagrams, optional elements / components and optional steps may be included within dashed boxes. [Brief explanation of the drawings]

[0108] [Figure 1] 1A-1B are simplified diagrams of a looped moving element wrapped around two and three pulleys, respectively. [Figure 2] FIG. 2A is a simplified diagram of a single optical sensor positioned to capture a side image of a section of a pulley belt according to respective embodiments of the present invention, and FIG. 2B is a simplified diagram of two optical sensors positioned to capture a side image of each section of a pulley belt according to respective embodiments of the present invention. [Figure 3] 3A-3B are simplified block diagrams of monitoring systems for monitoring a looped moving element, according to respective embodiments of the present invention. [Figure 4] FIG. 4 is a simplified block diagram of a monitoring system for monitoring a looped moving element, according to respective embodiments of the present invention. [Figure 5-1] 5A-5C are simplified exemplary side views of a loop-shaped movable element at different times. [Figure 5-2] FIG. 5D is a simplified side view illustrating the range of movement during the period of the loop element of FIGS. 5A-5C, and FIG. 5E is a simplified side view illustrating the range of secondary movement during a section of the loop element. [Figure 6-1]6A-6C are simplified illustrative views of the top surface of the loop-shaped movable element at each point in time, and FIG. 6D is a simplified top view illustrating the range of lateral movement of the loop-shaped element during a segment. [Figure 6-2] FIG. 6E is a simplified side view illustrating the undulating range of motion of a loop element, and FIG. 6F is a simplified schematic diagram of an example of a rotationally movable element. [Figure 6-3] 6G-6H are simplified schematic diagrams of examples of rotary movable elements. [Figure 7] FIG. 7 is a simplified flow diagram of a method for monitoring a looped moving element, according to respective embodiments of the present invention. [Figure 8] FIG. 8 is a simplified flow diagram of a method for monitoring a looped moving element, according to respective embodiments of the present invention. [Figure 9] FIG. 9 is a schematic diagram of a system for monitoring potential faults in moving and / or rotating elements, according to some exemplary embodiments of the present invention. [Figure 10] FIG. 10 is a simplified flowchart of a computer-implemented method for monitoring potential faults in moving elements, according to some exemplary embodiments of the present invention. [Figure 11] FIG. 11 is a simplified schematic block diagram of a method for monitoring potential faults in moving elements, according to some exemplary embodiments of the present invention. [Figure 12] FIG. 12 is a schematic block diagram of a system for monitoring potential faults in moving elements, according to some exemplary embodiments of the present invention. [Figure 13] FIG. 13 is a simplified block diagram of a system for monitoring the status and / or integrity of operation of moving elements, according to some exemplary embodiments of the present invention. [Figure 14] FIG. 14 is a simplified flow diagram of a method for monitoring the status and / or integrity of operation of a moving element, according to some exemplary embodiments of the present invention. [Figure 15]15A-15B are simplified diagrams of monitoring tension and / or tension in a belt coupled between two pulleys, according to respective exemplary embodiments of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0109] Various embodiments of the present invention are described below with reference to the drawings, which are to be considered in all respects only as illustrative and not in any way restrictive.

[0110] The elements illustrated in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the invention. Moreover, two different objects in the same drawing may be drawn to different scales.

[0111] The present disclosure, in some embodiments thereof, relates to monitoring of moving elements, and more particularly, but not exclusively, to monitoring of high speed moving elements, such as loop or rotary moving elements.

[0112] In some embodiments of the invention, the moving element is a loop moving element as described above, and the term "secondary movement" refers to movement other than longitudinal movement of the loop moving element.

[0113] In an alternative embodiment of the invention, the moving element is a rotationally moving element as described above, and the term "secondary movement" refers to movement that is not in the rotational direction of the rotationally moving element.

[0114] According to an embodiment of the present invention, a looped moving element is a mechanical element that is typically wound around one or more pulleys to transmit force from one rotating shaft to another. Another use for a looped moving element is to provide a source of movement (e.g., as a conveyor belt). Looped moving elements are often formed from flexible materials, such as plastic or rubber. However, cables, straps, ropes, chains, and the like can also be used.

[0115] The looped moving element may be wrapped around two or more pulleys and may be flat or crossed. The longitudinal movement of the looped moving element is determined by the relative positions of the pulleys and is different along different sections of the looped moving element.

[0116] Reference is now made to Figure 1A, which is a simplified diagram of an exemplary two-pulley system 100 having a pulley belt 110. The pulley belt 110 is wrapped around two pulleys, 120 and 130. For purposes of illustration, the pulley belt 110 is moving in a clockwise direction. As illustrated by the arrows, the upper and lower portions of the pulley belt 110 move in opposite longitudinal directions.

[0117] Reference is now made to FIG. 1B, which is a simplified diagram of an exemplary three-pulley system 150 with pulley belt 160. Pulley belt 160 is wrapped around three pulleys, 170, 180, and 190. For purposes of illustration, the pulley belt is shown moving in a clockwise direction. As illustrated by the arrows, the longitudinal direction of the pulley belt between each pulley pair is different for different portions of pulley belt 110.

[0118] In the non-limiting example of Figures 1A-1B, pulleys 120, 130, 170, 180, and 190 are illustrated as all the same size, although other systems may have pulleys of different sizes and / or positioned at asymmetric distances from one another.

[0119] Some embodiments presented herein analyze captured image data of the looped moving element to accurately monitor the operation and health of the looped moving element, which analysis may detect immediate problems and / or predict future problems with the looped moving element and / or associated elements.

[0120] Image data is captured by one or more optical sensors positioned within view of the looped moving element or a segment thereof.

[0121] Reference is now made to Figure 2A, which is a simplified diagram of a single optical sensor positioned to capture a side image of a segment of a pulley belt, in accordance with an exemplary embodiment of the present invention. Pulley belt 200 is wrapped around pulleys 210 and 220. Figure 2A shows a top-down view of pulley belt 200, with optical sensor 230 collecting image data of segment 1 from the side of belt 200.

[0122] Reference is now made to Figure 2B, which is a simplified diagram of two optical sensors positioned to capture side images of respective segments of a pulley belt 200, in accordance with an exemplary embodiment of the present invention. Pulley belt 200 is wrapped around pulleys 210 and 220. Optical sensor 240 collects image data for segment 2, and optical sensor 250 collects image data for segment 3.

[0123] Some embodiments of the invention presented herein monitor the operation of the looped moving element (and / or associated elements) during operation, optionally continuously or in response to faults, failure modes and / or failures detected in the looped moving element and / or associated elements.

[0124] As used herein, according to some embodiments of the present invention, the term "movable element" means an element configured to move in a substantially continuous, repetitive motion.

[0125] As used herein, according to some embodiments of the present invention, the term "fast moving element" refers to a moving element that is moving at a speed that is too slow for the optical sensor used to capture a clear image of the moving element. The term "fast moving element" encompasses both loop moving elements and rotary moving elements.

[0126] As used herein, according to some embodiments of the present invention, the terms "pulley belt" and "belt" refer to a loop of flexible material wrapped around two or more pulleys. Examples of pulley belts are provided below.

[0127] As used herein, according to some embodiments of the present invention, the term "associated element" refers to any element whose performance and / or health is affected by the looped moving element. Examples of such elements may include, but are not limited to, peripheral components, machines, vehicles, mechanisms, and / or other types of systems not explicitly listed herein.

[0128] As used herein, according to some aspects of the present invention, the terms "longitudinal direction of motion" and "longitudinal" refer to the direction in which a looped movable element moves from one pulley to the next pulley in the loop.

[0129] As used herein, in accordance with some aspects of the present invention, the term "transverse direction" refers to a direction at an angle to the longitudinal movement of a looped moving object (i.e., a secondary movement of the looped moving object). For example, vibrations in a pulley belt moving in a horizontal longitudinal direction may be seen from the side as a secondary (vertical) movement of the belt. In another example, the sliding of a rope on a pulley may be seen from above as a lateral movement along the width of the pulley.

[0130] As used herein, in accordance with some aspects of the present invention, the terms "range of movement in a secondary direction" and "range of movement" refer to the maximum amplitude of movement of a moving object in a secondary direction over a period of time (e.g., exposure time of an image or length of a video sequence of images).

[0131] As used herein, in accordance with some aspects of the present invention, the term "image data" refers to any output of an optical sensor, including images and / or data associated with images, that can be processed to estimate the quadratic range of motion.

[0132] As used herein, according to some embodiments of the present invention, the term "optical sensor" refers to a device that detects an optical signal and outputs an electronic signal in response thereto. Optionally, the optical sensor is a camera and the output signal is an image. Alternatively or additionally, the electronic signal output by the optical sensor is processed to form image data.

[0133] As used herein, according to some embodiments of the present invention, the term "optical signal" encompasses ultraviolet (UV), visible and infrared (IR) radiation as well as electromagnetic radiation in other frequency bands.

[0134] The optical sensor may include filter coatings for all or part(s) of the spectrum of visible and non-visible wavelengths.

[0135] As used herein, according to some embodiments, the term "fault" refers to an abnormality or undesired effect or process in the looped moving element and / or pulley and / or associated elements (such as the machine of which the looped moving element is part) that may or may not escalate into a failure but requires follow-up to analyze whether any component should be repaired or replaced. According to some embodiments, a fault may include, among others, a change in length (increase or decrease), a structural deformation, a surface deformation, a crack, a crack propagation, a defect, an expansion, a bending, abrasion, corrosion, discoloration, a change in appearance, and the like, or any combination thereof.

[0136] As used herein, according to some embodiments of the present invention, the term "failure" refers to any problem that may cause the looped moving element and / or pulley and / or associated element(s) to not operate as intended. In some cases, the failure may render the looped moving element and / or pulley(s) and / or other associated element(s) unusable or may even pose a danger to the associated elements or users.

[0137] As used herein, according to some embodiments of the present invention, the term "failure mode" should be interpreted broadly to encompass any manner in which a fault or failure may occur, such as rupture (partial or complete), separation of the looped moving element from the pulley, movement of the pulleys from their correct position, dislocation of the belt, change in shape (e.g., length), structural deformation, surface deformation, crack, crack propagation, defects, swelling, bending, wear, corrosion, discoloration, appearance change, and the like, or any combination thereof. It is understood that a single part may be subject to multiple failure modes related to different properties or its functionality.

[0138] Some failure modes may be common to different element types, while others may be more specific to one or more element types. For example, a rupture may be associated with the pulley belt, while a bend may be associated with the pulley shaft, and a corrosion failure mode may be associated with the chain.

[0139] For example, a failure mode of a looped moving element according to an embodiment of the present invention may be an increase in the length of the looped moving element, indicating a decrease in tension in the looped moving element. Depending on the length of the looped moving element, failure may be a slight elongation, such as a 1 cm change, or failure may be a significant elongation, such as a 10 cm change.

[0140] As used herein, in accordance with some embodiments of the present invention, the term "trend" should be interpreted broadly to encompass any behavior of a fault, or failure mode, over time, when or under what circumstances a fault turns into a failure. Trends are optionally associated with additional circumstances, such as environmental conditions, device usage characteristics, device user characteristics, or the like.

[0141] As used herein, according to some embodiments of the present invention, the terms "real-time" and "real-time control" mean within a period of time short enough to allow a moving element to respond while the machine in which it is installed is still operating, the duration being typically very short and, optionally, specified or otherwise determinable.

[0142] The maximum period suitable for real-time control is implementation dependent. Image data input and indicator output are performed during operation to prevent breakdowns by allowing continuous operation (if necessary) until maintenance can be performed.

[0143] Examples of real-time control include, but are not limited to: i. Less than 10 seconds after the indicator is output; ii. Less than one minute after the indicator is output; iii. Less than the estimated time to failure.

[0144] The principles, applications, and implementations of the teachings herein may be better understood with reference to the accompanying description and drawings. Upon perusal of the description and drawings presented herein, those skilled in the art will be able to implement the teachings herein without undue effort or experimentation.

[0145] Before describing at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and arrangement of components and / or methods set forth in the following description and / or illustrated in the drawings and / or examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

[0146] For clarity, some embodiments of the present invention are illustrated and described using a pulley belt as the looped moving element, however, the use of a pulley belt as the looped moving element is non-limiting and other types of elements may be used to the extent possible for a particular embodiment.

[0147] I. Surveillance System Reference is now made to FIGS. 3A-4, which are simplified block diagrams of monitoring systems for monitoring moving elements, according to respective embodiments of the present invention.

[0148] As described below, embodiments of the monitoring system may be employed for many purposes, including, but not limited to: 1) monitoring the health of moving elements; 2) monitoring the function and / or health of moving elements and associated components (e.g., pulleys, rotating shafts, machinery, vehicles, aircraft, heating, ventilation and air conditioning systems, manufacturing systems, etc.); 3) predicting potential failures of looped moving elements; 4) predicting potential failures of related or surrounding components; and 5) Determining when maintenance is or will be required for one or more moving elements, associated elements, mechanisms, machines, etc.

[0149] As used herein, according to some embodiments, the term "health" of an element refers to the overall condition, functionality, and status of that particular element. It encompasses an assessment of various operational parameters, metrics, or data points that indicate the element's current state, performance, ability to operate as intended, and predictions of future operation and status.

[0150] In some embodiments, the operating parameters, metrics and / or data points used to assess the health of an element are based on instructions and / or guidelines provided by a manufacturer, a user, or the like.

[0151] According to the embodiment of FIG. 3A , to monitor moving elements, monitoring system 310 includes processing circuitry 320. Processing circuitry 320 includes one or more processors 330 and, optionally, additional electronic circuitry. Processor(s) 330 process the image data to perform the analyses described herein. Processor(s) 330 may also perform other tasks, such as storing the image data in memory, providing a graphical user interface (GUI) to a user, processing input from the GUI and / or other input / output means, and exporting data to external systems (e.g., a controller for the monitored system, a remote computing platform, and / or a predictive health maintenance system).

[0152] The processor(s) 330 may include one or more of a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), a microprocessor, an electronic circuit, an integrated circuit (IC), or the like.

[0153] Optionally, the processing circuitry is in communication with the optical sensor(s) via wireless communication (e.g., Bluetooth, cellular networks, satellite networks, local area networks, etc.) and / or wired communication (e.g., telephone networks, cable television or internet access, and fiber optic communication, etc.).

[0154] In some embodiments, processing circuitry 320 is located in a single location as shown in Figures 3A-3B for clarity.

[0155] In alternative embodiments, the processing circuitry is distributed across multiple locations. Optionally, at least one optical sensor includes processing circuitry that performs at least a portion of the processing described herein.

[0156] Optionally, some or all of the processing circuitry is located remotely, for example in a controller for the moving element being monitored.

[0157] Optionally, monitoring system 310 further includes memory 340 for internal storage of data for use by monitoring system 310. Memory 340 may be a hard disk drive, a flash disk, random access memory (RAM), memory chips, or the like.

[0158] Stored data may include, but is not limited to: a) image data; b) Data associated with the image(s). Examples of associated data may include, but are not limited to, the time of capture of the image, the environmental conditions at the time of image capture, the operational and other parameters of the machine / device / system in which the moving element is operating; c) program instructions for execution by processor(s) 330; d) Algorithms and rules for monitoring moving elements; e) failure modes of moving elements; and f) A model of the mechanism, optionally developed by machine learning from a training set of images of the mechanism or similar mechanism(s). For example, the model may input images of segments of moving elements and output one or more of: range of motion in a secondary direction, health of the moving element, health of elements using the moving element, fault alerts, maintenance instructions, etc.

[0159] In some embodiments, data generated by the monitoring system 310 is exported to one or more external platforms, stored on cloud storage, or the like.

[0160] Optionally, the processing circuit 320 further includes one or more interface(s) 350 for inputting and / or outputting data. For example, the interface(s) may serve to input image(s), and / or communicate with other components within the machine, and / or communicate with external machines or systems, and / or provide a user interface.

[0161] In one example, indicators and information regarding moving elements, related elements, etc. are provided via interface(s) 350 to a HUMS, CBM, or similar system.

[0162] 3B, monitoring system 310 further includes one or more optical sensors 360.1-360.n that provide image data used to monitor the moving elements. Optionally, optical sensors 360.1-360.n provide the image data to processing circuit 320 via data bus 370.

[0163] According to some embodiments, optical sensors 360.1-360.n may include cameras. According to some embodiments, optical sensors 360.1-360.n may include electro-optical sensors. According to some embodiments, optical sensors 360.1-360.n may include any one or more of charge-coupled devices (CCDs), light-emitting diodes (LEDs) and complementary metal-oxide semiconductor (CMOS) sensors (or active pixel sensors), photodetectors (e.g., IR sensors, visible light sensors, UV sensors), distance measurement sensors such as lidar sensors, or any combination thereof. According to some embodiments, optical sensors 360.1-360.n may include any one or more of point sensors, distributed sensors, extrinsic sensors, intrinsic sensors, transmissive sensors, diffuse reflective sensors, retroreflective sensors, or any combination thereof.

[0164] Optionally, processing circuitry 310 controls the operation of one or more of the optical sensor(s). Aspects of the operation of the optical sensors that may be controlled include, but are not limited to, the following: 1) Image capture time; 2) Exposure time / frame rate; 3) visual field; 4) Powering the optical sensor on and off (e.g., turning the optical sensor off when the moving element is not in motion).

[0165] Optionally, processing circuitry 320 controls one or more light sources, each illuminating at least a portion of the feature. Optionally, each light source is focused on a particular component or reference point, which may allow for reducing the required intensity of light.

[0166] Alternatively or additionally, the light source(s) are controlled by a user.

[0167] Optionally, the wavelength of the light source may be controlled by processing circuitry 320 and / or by a user.

[0168] Optionally, the light source may be configured to illuminate the movable element and / or sections thereof.

[0169] By controlling the light source, processing circuitry 320 and / or a user can improve image characteristics to facilitate image processing and analysis. For example, the light source can be adjusted to increase the contrast between moving elements and their surroundings. Alternatively or additionally, the light source can be adjusted to increase shadows that highlight areas, thereby facilitating detection of defects and / or surface and / or structural imperfections.

[0170] According to some embodiments, the light source(s) include one or more of a light bulb, a light emitting diode (LED), a laser, an electroluminescent wire, and light transmitted via a fiber optic wire or cable (e.g., from an LED coupled to a fiber optic cable). Other types of light sources may also be suitable.

[0171] Optionally, processing circuitry 320 controls one or more of the following: 1) The direction of light source; 2) duration of irradiation; 3) irradiation frequency; 4) Irradiation intensity; 5) switching a light source on or off (e.g., synchronizing illumination with image capture times by an optical sensor, perhaps to create a strobe effect); and 6) Changing the wavelength of irradiation.

[0172] According to some embodiments, the light source may emit visible light, infrared (IR) radiation, near-IR radiation, ultraviolet (UV) radiation, or light within any other spectrum or frequency range that is visible by at least one optical sensor.

[0173] Optionally, at least one optical sensor may include a filter coating for all visible and non-visible wavelengths.

[0174] According to some embodiments, the light source is a light source configured to illuminate with a strobe or short pulses. According to some embodiments, the light source may be configured to emit strobing light without the use of a shutter (such as a global shutter, a rolling shutter, a shutter or any other type of shutter).

[0175] The use of a strobe can be particularly useful when it is desirable to obtain a clear image of a fast-moving object (such as an object moving at a high longitudinal velocity), and is optionally described in U.S. Provisional Patent Application No. 63 / 394,150, and U.S. Provisional Patent Application No. 63 / 521,140, ​​and corresponding PCT applications filed on the same day as this PCT application, all of which are incorporated herein by reference in their entireties. Defects such as tears and localized variations in the belt surface may be easier to identify with a clearer image.

[0176] Optionally, processing circuitry 320 selects optimal settings for each light source(s) based on a predefined algorithm. Optionally, the light sources are controlled according to the environment in which the monitored system is currently operating. For example, the light sources may be turned on during nighttime operations and turned off during the day.

[0177] Optionally, processing circuitry 320 dynamically changes light source operation during operation, for example, by using different fibers of a fiber optic cable to emit light at different times, or by emitting light from two or more fibers simultaneously.

[0178] Optionally, the light source is part of the surveillance system 310 .

[0179] According to some embodiments, one or more optical sensors may include one or more lenses and / or fiber optic sensors. According to some embodiments, optical sensors 360.1-360.n may include a software correction matrix configured to generate an image from the optical sensor output signals. According to some embodiments, one or more optical sensors may include a focus sensor configured to enable the optical sensor to adjust its focus based on changes in the acquired data. According to some embodiments, the focus sensor may be configured to enable the optical sensor to detect changes in one or more pixels of the acquired signals. Optionally, the change in focus may be used as further input data for processing circuit 320.

[0180] Reference is now made to Figure 4, which is a simplified block diagram of a system for monitoring moving elements, in accordance with an embodiment of the present invention. Figure 4 also illustrates external components with which monitoring system 400 may communicate, as described below.

[0181] The monitoring system 400 includes a processing circuit 410. Optionally, the monitoring system 400 includes one or more optical sensors 420 and / or one or more light sources 430.

[0182] Processing circuitry 410 receives image data of at least one segment of moving element 440 from optical sensor(s) 420. Processing circuitry 410 determines the range of movement in one or more secondary directions of the segment or segments of moving element 440 from the image data. Processing circuitry 410 outputs an indication of the health of moving element 440 based on an analysis of the range of movement in the secondary direction(s). Optional embodiments are described in further detail below.

[0183] Optionally, the movable element is a loop-shaped movable element, and the secondary direction of movement is perpendicular to the longitudinal movement (i.e., the primary direction of movement), for example, as shown in Figures 5A and 6A (described below).

[0184] The optical sensor(s) 420 capture image data for each section of the moving element 440 .

[0185] Optionally, processing circuitry 410 inputs image data from a single optical sensor in a fixed position relative to moving element 440. In an alternative optional embodiment, processing circuitry 410 inputs image data from multiple optical sensors, each capturing image data for a respective section of moving element 440.

[0186] Optionally, at least one of the segments is at least 1%, 5%, 10%, 25% or 50% of the total length of the movable element. Further optionally, at least one of the segments is between 1% and 10%, between 10% and 20%, or between 1% and 50% of the total length of the movable element.

[0187] Optionally, processing circuitry 410 controls optical sensor(s) 420 substantially as described above.

[0188] Optionally, processing circuitry 410 controls light source(s) 430 substantially as described above.

[0189] Optionally, processing circuitry 410 determines whether moving element 440 is moving and / or the speed at which moving element 440 is moving. Further optionally, processing circuitry 410 determines whether moving element 440 is moving based on information obtained from one or more of: another sensor (e.g., a motion sensor) and / or a system controller, and / or by analysis of image data (e.g., a blurry image may indicate that moving element 440 is moving, while a clear image may indicate that moving element 440 is stationary).

[0190] If the moving element is a looped moving element, the system controller may provide data indicating which section of the looped moving element is within the field of view of the optical sensor at a given time.

[0191] Optionally, processing circuitry 410 controls the timing of image data capture (i.e., optical sensor) based on the velocity of moving element 440. For example, images may be captured only while moving element 440 is moving. Alternatively or additionally, image capture may be adjusted to capture images of the entire moving element 440 or of a selected section thereof.

[0192] Optionally, the image data is tagged with information about the operating status of the moving element 440 and / or associated elements, such as whether the moving element 440 is in motion and / or the speed of the moving element 440. The tags may be used during analysis of the image data and / or for machine learning purposes. Optionally, the tags are displayed to the user on the GUI 470.

[0193] Optionally, monitoring system 400 includes additional elements, such as memory and / or interfaces (described with reference to FIGS. 3A-3B), which are not shown in FIG. 4 for purposes of clarity.

[0194] Optionally, monitoring system 400 provides the indicators to one or more external systems and / or devices, which take action based on the information contained in the indicators.

[0195] Optionally, monitoring system 400 provides the indicators to external controller 450. Optionally, external controller 450 analyzes the data contained in the indicators and performs selected control actions when the analysis indicates that such control actions are necessary. Alternatively or additionally, monitoring system 400 selects control actions to be performed (or recommended) and instructs external controller 450 to perform them.

[0196] External controller 450 may control moving element 440 and / or associated elements, such as the machine or vehicle containing moving element 440, elements near moving element 440, elements coupled to moving element 440, etc. Further optionally, the control action prevents movement of moving element 440 when a fault is detected.

[0197] Optionally, monitoring system 400 provides the indicators to predictive maintenance system 460. Predictive maintenance system 460 analyzes the data contained in the indicators along with additional data such as previously received indicators, manufacturer specifications, operational information (e.g., how long moving element 440 has been operating, environmental conditions, etc.). Based on this analysis, predictive maintenance system 460 provides maintenance instructions for moving element 440 and / or associated element(s) 455.

[0198] Optionally, monitoring system 400 provides indicators for display on a graphical user interface (GUI) 470. The indicators may alert a user to faults, failures, trends, and / or failure modes detected in moving element 440. The indicators may also alert a user that action is needed to maintain proper operation of moving element 440 and / or associated element(s) 455.

[0199] Optionally, monitoring system 400 provides control signals to moving element 440 and / or associated element(s) 455 to directly execute a control action on moving element 440 and / or associated element(s) 455. The appropriate control action is selected based on further analysis of the information in the indicator. Further optionally, the control action prevents operation of moving element 440 if a fault is detected.

[0200] Optionally, the monitoring system 400 outputs the image data, and optionally other data (analysis results, labels, etc.) to external storage 480.

[0201] Optionally, surveillance system 400 outputs the image data, and optionally other data, to machine learning system 490. Machine learning system 490 may update a training set for modeling the moving elements using the new image data (and / or along with analysis results, labels, etc.). The updated training set may then be used to retrain the model.

[0202] Optionally, image data is not input during periods when the moving elements are not in motion. Alternatively or additionally, image data is input when the moving elements are idle and / or not moving to form a baseline for comparison with images captured during motion.

[0203] Optionally, the movement range analysis is performed by the processing circuitry only on image data collected during operation. Data may be continuously input from the optical sensor, but not all data is analyzed by the monitoring system. Image data collected during periods of non-operation may be discarded or exported by the monitoring system to an external system (e.g., to an external controller) and / or for external storage (e.g., to cloud storage).

[0204] II. Loop-shaped moving element Optionally, the looped moving element is a pulley belt. Many types of pulley belts are used in various mechanisms, machines, vehicles, etc. Examples of pulley belts include: 1. V-belt - A trapezoidal cross-section belt designed to fit over a V-shaped pulley. V-belts are used in a variety of applications, including automotive engines, industrial machinery, and HVAC (heating, ventilation, and air conditioning) systems. 2. Timing belt - A belt with teeth on the inside that match the teeth on a pulley. Timing belts are used in applications requiring precise synchronization of shafts or high torque transmission, such as automobile engines and robotics. 3. Flat Belt - A rectangular cross-section belt used in applications where a wide, flat surface is required for power transmission such as conveyor systems, printing presses, and textile machinery. 4. Serpentine Belt - A flat belt with multiple grooves on one side. They are used in automobile engines to drive various components such as alternators, power steering pumps, and air conditioning compressors. 5. Round Belts - Circular cross-section belts used in applications requiring high flexibility and minimal vibration. They are often used in conveyor systems, power transmission in small appliances, and in some types of sophisticated machinery.

[0205] Optionally, the looped movable element is a cable.

[0206] Optionally, the looped movable element is a strap.

[0207] Optionally, the looped movable element is a rope.

[0208] Optionally, the looped movable element is a chain.

[0209] III.Indicators The indicators may provide many types of information regarding various aspects of the health of the moving element, non-limiting examples include the range of motion of the moving element, anomalies detected in the range of motion of the moving element, the structure of the moving element, defects detected in the moving element, health assessments, alerts, and maintenance-related information.

[0210] Indicators that provide information regarding the movement of the moving element may include, but are not limited to: 1) The magnitude of the range of movement of one or more segments of the moving element; 2) the estimated tension in the looped moving element; 3) the rate of change of the range of movement of the moving element over time; 4) the period for the moving element to move through its entire range of motion; 5) the rate of change of the period for the moving element to move through its entire range of motion; 6) The difference between the expected and detected range of movement.

[0211] Health-related indicators may include, but are not limited to: 1) Integrity of moving elements; 2) the health of machines utilizing moving elements; 3) the health of vehicles utilizing moving elements; 4) the integrity of mechanisms utilizing moving elements; 5) the integrity of the relevant components; and 6) The health of other sensors located near moving elements, such as temperature sensors, vibration sensors and others.

[0212] Maintenance-related metrics may include, but are not limited to: 1) Maintenance instructions; 2) time-to-failure estimation; 3) fault alerts; and 4) Operational instructions in response to detected faults.

[0213] IV. Determining the range of movement of the moving element Optionally, the range of motion of the moving element is determined from one or more blurred images of the moving element. The blurred image(s) are captured by at least one optical sensor using an exposure time that is too long to capture a clear image of the moving element as it moves. Images of sufficient quality for analysis can be captured with a standard camera, and no high performance optical sensor is required. Examples of determining the range of motion from blurred images for looped and rotary moving objects are presented in more detail below.

[0214] Optionally, an exposure time of at least one optical sensor is selected to create motion blur of said moving element in image(s) of said moving element captured by that optical sensor.

[0215] In some embodiments, the motion range is determined from a single image: if the periodic motion of a moving object (e.g., a looped moving object) is fast relative to the exposure time of the optical sensor, enough repetitions can be captured in a single frame so that the entire amplitude of the motion range can be seen and analyzed in the blurred trace without requiring more than one horizontal frame.

[0216] Typically, standard optical sensors are too slow to capture a clear, blur-free image of a fast-moving object. The minimum exposure time for a standard CMOS sensor at 30 frames per second (FPS) is typically 1 / 300 of a second. However, this is only a theoretical limit. In practice, the minimum required exposure time may be longer to avoid poor lighting conditions.

[0217] For example, in an embodiment where each frame is captured in 1 / 100th of a second (approximately 0.01 seconds). Typically, motion blur of one pixel in the image can still be considered reasonably sharp. If we consider the width of a pixel as the maximum linear distance an object can move within one frame, then assuming the width of one pixel in the image is 0.1 millimeters (0.0001 meters): Maximum speed (m / sec)=0.0001m / 0.01sec≒0.01m / sec Therefore, for a 30 fps camera, objects moving faster than approximately 0.01 meters / second (or 10 millimeters / second) are likely to result in easily noticeable motion blur in the captured images.

[0218] Alternatively or additionally, the range of motion is determined from multiple images, optionally a video sequence of images taken at different times by the same optical sensor (e.g., a 2 second video sequence containing 50-60 frames).

[0219] Alternatively or additionally, the range of motion is calculated by statistical analysis of the image data of the series of images, for example using statistical or clustering techniques such as principal component analysis (PCA).

[0220] Optionally, the color and / or brightness and / or blurred appearance of the moving object in the image data captured during the periodic motion is analyzed to detect changes in the speed and frequency of the moving object's movement and / or its acceleration or deceleration.

[0221] Optionally, the illumination source is in a fixed position relative to the movable element 440 .

[0222] The inventors have found that the number of times an object is captured in a frame changes the color and blurred appearance of the image because each time an object passes through the frame, it adds light to the sensor, making it appear brighter.

[0223] Optionally, information about the movement of the moving element (e.g., velocity) is extracted from the image data by analyzing image characteristics (such as color and brightness) and / or shape of the blurred moving element in the image. The effect of the movement of the moving element on the image characteristics and shape of the blurred moving element in the image is described below.

[0224] The amount of color change depends on the speed of the object, the exposure time of the camera, and the sensitivity of the camera sensor. If the moving object is moving very fast or the exposure time is very short, the object may appear to be a single color. However, if the object is moving more slowly or the exposure time is longer, the object may appear to have a gradient of color, with the brightest parts of the object being those that pass through the frame multiple times.

[0225] Additionally, if a moving object is moving quickly and passing through the frame multiple times, the outline of its blurred shape in the image will change depending on the speed and frequency of the moving object's movement.

[0226] For example, if an object is moving at a constant speed, the blurred image will be uniform across the moving object, but if the moving object is accelerating or decelerating, the blur will be more noticeable in front or behind the moving object, depending on the direction of acceleration.

[0227] If the moving object is moving at a constant speed, the blurred image will be uniform across the object, but if the object is moving at a varying speed, the blur will be more noticeable at the edges of the object because the object is moving at different speeds at different times in its cycle.

[0228] Optionally, the monitoring is performed automatically when the moving element is moving, and further optionally, the monitoring is stopped automatically when the moving element stops moving.

[0229] Optionally, at least one optical sensor does not view the moving element from a normal direction, and the image data is processed to correct for distortions caused by the angle between the optical sensor and the moving object. For example, techniques such as photogrammetry and dimensional analysis use known measurements of objects or reference marks in an image to calculate the size of other objects, as long as there are other elements in the image with known size, distance, or perspective that can serve as a reference or magnification.

[0230] IV.1. Determining the range of motion of a looped moving element According to some embodiments of the present invention, a monitoring system assesses the health of a looped moving element by analyzing the range of motion of the looped moving element in one or more secondary directions of motion.

[0231] There are known formulas for calculating the deflection of a looped moving element when it is not moving, based on the span length and other relevant parameters. However, additional factors, such as the load on the driven pulley, the belt tension, and centrifugal forces, come into play when the looped moving object is moving and rotating. This complicates the analysis and makes it impractical to calculate the deflection range while the system is running.

[0232] An embodiment of the present invention determines the range of motion of a looped moving element from image data collected relative to the looped moving element while it is moving.

[0233] An example of secondary movement is the upward and / or downward movement of the looped moving element relative to the surface of the looped moving element during normal operation (also referred to herein as vertical movement). The range of vertical movement may be expected to be greater when the looped moving element is operating under lower tension than when it is operating under higher tension. For example, in FIG. 5A, the primary movement is longitudinal movement (left arrow), and the secondary movement is transverse to the longitudinal movement (vertical arrow).

[0234] A second example of secondary movement is the side-to-side movement of the looped movable element on the pulley during normal operation (also referred to herein as lateral movement). The range of lateral movement can be expected to be greater when the looped movable element is operating under lower tension than when it is operating under higher tension. For example, in FIG. 6A, the primary movement is longitudinal movement (left arrow) and the secondary movement is lateral movement along the pulley (vertical arrow).

[0235] For illustrative purposes, simplified examples of primary and secondary movement of a loop-shaped movable element are shown in Figures 5A and 6A.

[0236] Reference is now made to FIGS. 5A-5C, which are simplified, illustrative diagrams of the loop-shaped moving element as viewed from the side at different times. Note that FIGS. 5A-5C do not show image blurring and therefore cannot be used to determine the range of movement when a determination is made from a blurred image, but may be used in conjunction with other image analysis techniques. In FIG. 5A, the top surface of the loop-shaped moving element 500 is flat and horizontal (as indicated by dashed line A), which may be considered the normal state. FIGS. 5B-5C show the loop-shaped moving element 500 with upward and downward movement, respectively. In FIG. 5B, the maximum height of the loop-shaped moving element 500 is d1 relative to the expected height (as indicated by dashed line B). In FIG. 5C, the minimum distance of the loop-shaped moving element 500 below the expected height is d2 (as indicated by dashed line C). Therefore, the maximum range of movement of the loop-shaped moving element 500 in the vertical direction is d1 + d2. FIG. 5D shows a blurred outline of the looped moving element created by capturing image data using a long exposure time simultaneously with the looped moving element.

[0237] Reference is now made to Figures 6A-6C, which are simplified exemplary diagrams of the loop moving element viewed from above (i.e., with the optical sensor pointed downward toward loop moving element 610) at each point in time. Note that Figures 6A-6C do not show image blurring and therefore cannot be used to determine range of motion when the determination is made from a blurred image, although it may be used in conjunction with other image analysis techniques.

[0238] In Figure 6A, loop-shaped movable element 610 is centered over pulleys 620 and 630 (as shown by dashed line D), which may be considered the normal state. Figures 6B-6C show loop-shaped movable element 600 with lateral movement across pulleys 620 and 630. In Figure 6B, loop-shaped movable element 600 is displaced by d3 relative to the center (as shown by dashed line E). In Figure 6C, loop-shaped movable element 600 is displaced by d4 relative to the center (as shown by dashed line F).

[0239] Figure 6D shows the blurred outline of the loop moving element in the image. The blur effect was created by capturing image data simultaneously with the loop moving element using a long exposure time. The total lateral range of movement of the loop moving element 600 is d3 + d4, see 650 in Figure 6D.

[0240] In some embodiments of the invention, the exposure time of at least one optical sensor capturing images of the moving element is therefore equal to or exceeds the expected time it takes for the moving element to move through its entire range of motion (e.g., from B to C in FIGS. 5B-5C, or from D to E in FIGS. 6B-6C). Further optionally, the processing circuitry controls the exposure time of the optical sensor.

[0241] A relatively long exposure time creates a blurring effect on the section of the belt within the field of view of each optical sensor, as shown in Figures 5E and 6D. The portion of the image showing the belt appears as a two-dimensional shape where there is transverse movement (vertical and / or lateral) of the belt. Thus, an optical sensor capturing an image of section 4 in Figure 5D would include shapes spanning B-C, and an optical sensor capturing an image of section 5 in Figure 6C would include shapes spanning E-F.

[0242] 5E shows a simplified image 540 captured by an optical sensor (not shown) looking at the looped moving element from the side. Due to the relatively long exposure time, the total range of motion over a limited segment of the belt (segment 4) is visible in the image as 550. The range of motion of the looped moving element can be calculated as the maximum distance between the top and bottom of the blurred portion of the moving element in the image (e.g., 550 in FIG. 5E).

[0243] 6D shows a simplified image 640 captured by an optical sensor (not shown) pointing downward toward the top surface of the loop-shaped moving element. Due to the relatively long exposure time, the total range of motion over a limited segment of the belt (segment 5) is visible in the image as 650.

[0244] Note that 550 and 650 indicate, although not necessarily in absolute terms (e.g., in mm), the range of movement of the optical sensor relative to the field of view. Image processing can be performed on image 540 to account for factors such as the field of view, the field of view angle, the distance of the optical sensor from the moving loop element, the angle of the optical sensor relative to the moving loop element, and other factors.

[0245] Figures 5A-6D illustrate situations where the secondary range of motion is relatively simple. For example, Figures 5B-5C illustrate a situation where the lateral motion forms a single arc between two pulleys. Figures 6B-6C show that the looped moving element moves evenly on both pulleys, so the blurred portion of the image showing this motion is rectangular. However, the transverse motion of the moving element can be more complex.

[0246] If a looped moving element (such as a belt between two pulleys) is not properly tensioned, it may sag or move in a transverse motion. This sag may be seen as a multi-peak wave or multi-peak waveform between the two pulleys. The belt may form several peaks as it sags and moves in a non-linear path due to lack of tension. The shape of the wave, its length (distance between peaks), amplitude (height of the peaks), and its location may depend on factors such as: a. Travel speed and belt tension; b. distance between pulses; c. The coefficient of friction between the belt and the pulley; d. Pulley arrangement; e. Belt wear and tear; and f. Temperature of the environment.

[0247] To elaborate, reference is now made to Figure 6E, a simplified exemplary diagram of a looped moving element undergoing multi-arc motion between two pulleys. In Figure 6E, the vertical motion profile has a three-peaked wave-like shape, as exemplified by three substantially oval segments 530.1-530.3. Thus, imaging too narrow a segment of the moving element (e.g., segment 6) may not capture the entire range of motion.

[0248] More complex situations can arise, for example the wave-like shape can change over time, changing the position(s) it undergoes over its range of motion.

[0249] Optionally, the minimum width of the image data used to determine the range of motion is equal to or exceeds the distance between two peaks (e.g., between two highest levels). Therefore, the image data may be sufficient to determine the entire range of motion of the movable element. Note that the sections of the image do not necessarily need to be aligned with the peaks (as in section 7), but may be offset (as in section 8).

[0250] Optionally, the image data includes multiple images captured by different optical sensors imaging substantially adjacent sections of the moving element, which together span a desired distance along the moving element.

[0251] IV.2. Determining the range of motion of a rotary moving element According to some embodiments, the moving element is a rotating moving element such as a shaft, rotor, propeller, fan blade, impeller, turbine blade and / or turbo blade.

[0252] Optionally, the monitoring is performed automatically when the moving element is moving, and further optionally, the monitoring is stopped automatically when the moving element stops moving.

[0253] Optionally, the range of motion is determined from a single image.

[0254] Alternatively or additionally, the range of motion is determined from multiple images, such as a video sequence of images (eg, a 2 second video sequence containing 50-60 frames).

[0255] Optionally, the monitoring system assesses the health of the rotatable moving element by analyzing changes in the range of motion of segments of the rotatable moving element in images taken with a relatively long exposure time compared to clearer image(s) and / or compared to images taken while the rotatable moving element is not moving.

[0256] Optionally, the color change is used to detect changes in the speed, frequency, etc. of rotation of a moving element (eg, the rotational speed of a propeller or turbine blade).

[0257] Alternatively or additionally, irregularities are detected by statistical analysis of the image data of the series of images, for example using statistical or clustering techniques such as principal component analysis (PCA).

[0258] Reference is now made to Figures 6F-6H, which are simplified schematic diagrams of three example rotatable moving elements. The right side of each figure shows the rotatable moving element when stationary. The left side of each figure shows the outline of the rotatable moving element, caused by the blurring effect of capturing image data of the rotatable moving element with a long exposure time while it is moving.

[0259] 6F is a simplified diagram of a shaft 6000 attached to one element 6001 (e.g., a rotating plate). The shaft 6000 is not curved or bent. Because the shaft is straight, an image of the shaft rotating about its own axis taken with a long exposure time (left side of the figure) is substantially the same as an image of the rotating shaft taken with a short exposure time or when the shaft is stationary (right side of the figure).

[0260] However, if the shaft has protrusions or other irregularities, an image of the shaft taken with a long exposure time (e.g., at least equal to the time it takes to complete a complete rotation of the shaft) will differ from an image captured when the shaft is stationary (or taken with a short exposure time), as illustrated in Figures 6G-6H.

[0261] Figure 6G is a simplified diagram of a shaft 6001a with a curve at its end 6002a. A long exposure image of the shaft 6001a while it is rotating (left side of Figure 6G) shows that the shaft has a different shape 6002b in the curved region, as indicated by the greater range of motion (i.e., the greater difference between the top and bottom of the curved section) than the shaft height at 6001a. The unbent portion of the shaft remains the same size and shape in both images.

[0262] Figure 6H is a simplified diagram of a shaft 6011a with a curvature in the center 6012a. A long exposure image of the shaft 6011a while it is rotating (left side of Figure 6H) shows that the curved region 6012b has a different shape, as indicated by the greater range of motion (i.e., the greater difference between the top and bottom of the irregular segment) than the height of the shaft in 6011a. The uncurved portion of the shaft remains the same size and shape in both images.

[0263] According to some embodiments, at least one optical sensor is configured to capture one or more images of the rotating element as it rotates. The images are captured by the optical sensor(s) at a shutter exposure time longer than that required to capture a clear image, or at a frames per second (FPS) rate lower than that required to capture a clear image (e.g., a standard 30, 60, or 120 FPS optical sensor). This creates a blurring effect in locations with irregularities.

[0264] Optionally, the processing circuitry is configured to detect undesirable irregularities in the rotatably moving element by analyzing changes in size and / or shape of one or more sections of the shaft within the image. The changes in size and / or shape may indicate a fault or failure in the rotatably moving element or related components. For example, a fault may be detected if the increase in height of a section of the shaft is greater than a predefined threshold. The indicator may include information that a fault / failure / tendency toward failure / etc. has been detected and optionally further data (such as the size of the irregularity). Some irregularities may be caused by the normal shape of the rotatably moving element, but these may be ignored, if necessary, when assessing the health of the moving object.

[0265] V. Health Assessment Analysis The range of motion and / or changes in range of motion over time provide information about the health of the moving element and / or associated elements. Examples include, but are not limited to: 1) Tension - A greater range of movement may reflect the looped moving element operating under less tension, and a smaller range of movement may reflect the looped moving element operating under more tension. 2) Material Integrity - The range of movement can be affected by the integrity of the material forming the moving element. Deterioration of the material (e.g., fracture) can cause a decrease or increase in the range of movement. 3) Related Elements - The range of motion can be affected by the operation of related elements. For example, a change in the relative position of pulleys can cause a change in the range of motion of the moving elements wrapped around them.

[0266] Optionally, the assessment of the health of the moving element is based on thresholds. Thresholds and / or ranges may be defined for parameters such as the magnitude of the range of motion, the rate of change of the range of motion, and other parameters related to the range of motion. For example, a maximum threshold may be defined for the range of motion. If that threshold is exceeded, the analysis indicates a fault in the moving element.

[0267] Optionally, other data is used in the analysis, such as information provided by other sensors (e.g., vibration or temperature sensors), information about previous failures, device specifications, etc. This additional information may reduce false positives (e.g., fault alerts when there is no fault).

[0268] Optionally, assessing the health of the moving elements includes predicting the future health of the moving elements by performing a trend analysis on changes in the moving range over time. The trend analysis may be performed using any suitable technique known in the art. Examples of trend assessment techniques include moving averages, exponential smoothing, seasonal decomposition, autoregressive integrated moving averages (ARIMA), long short-term memory (LSTM) networks, and support vector regression (SVR).

[0269] Optionally, the assessment of the health of the moving element is based on the shape and / or shape changes of the moving element as seen in the image data. For example, a change in the height of a pulley belt that is not present in all images but rather appears in sync with the longitudinal movement of the belt may indicate a protrusion growing at that location. If the size of the protrusion exceeds an acceptable dimension, the protrusion may be considered a fault.

[0270] The results of the image analysis may be correlated with information from one or more other sensors or external sources. Non-limiting examples include: 1) Motion sensors (e.g., accelerometers, gyroscopes, magnetometers, magnetic compasses, vibration or tilt sensors); 2) Temperature sensor; 3) Control system information (e.g., moving element operating hours, load, time since last moving element replacement, etc.).

[0271] In one example, a motion sensor provides information about the time that a moving element is moving, and images from those times can be used to assess the health of the moving element.

[0272] In a second example, a temperature sensor provides information about the temperature of a moving element, the health of which may be expected to deteriorate faster if it is operating at a high temperature.

[0273] In a third example, the health of the moving elements may be expected to deteriorate over time since the last replacement.

[0274] VI. Indicator generation and output An indicator is generated based on the analysis of the range of motion. The indicator may be output to an external element as described herein and / or used by the processing circuit to control the moving element according to any of the embodiments described herein. The indicator may be formatted in any suitable format known in the art.

[0275] The data contained within the indicator may be tailored to the element to which it is being sent. For example, the indicator provided to the external controller may be a general health rating (e.g., on a numerical scale) for the moving element and an alert if a fault is detected. In a second example, the indicator displayed to the user may include more detailed information regarding range of travel in one or more directions, trend analysis, fault and fault alerts, etc., and / or maintenance instructions. In another example, the indicator to a predictive maintenance system may include only the current range of travel in one or more directions for further analysis by the predictive maintenance system.

[0276] Optionally, the indicator includes images and / or video of the moving elements, further optionally in slow motion to allow evaluation by a technician.

[0277] Optionally, the indicators include information received from other sources, such as external sensors, information from a control system, or the like.

[0278] The time(s) at which the analysis and generation of indicators is performed may be tailored to suit the needs of a particular system, machine, aircraft, etc. Examples of when the analysis and output of indicators may be performed include, but are not limited to, the following: 1) In progress; 2) Regularly. 3)Only during operation; 4) Both during operation and idle periods. 5) When problem indicators are received, for example, from other sensors in the system.

[0279] Optionally, if certain conditions occur (eg, high temperature), the analysis is performed more frequently.

[0280] Optionally, the indicators are used by a control system and / or a preventative maintenance system to determine whether further action should be taken (eg, decisions regarding operation and / or maintenance of moving elements).

[0281] 1. Data Structures Optionally, the indicator is obtained from a data structure indexed by at least one parameter determined from the analysis of the moving range and, optionally, additional information.

[0282] Parameters that may be used to obtain an index from the data structure include, but are not limited to: 1) The range of movement of one or more segments of a moving element; 2) the rate of change of the range of movement of the moving element across one or more segments of the moving element; 3) Trend(s) over time in the range of movement of the moving elements and / or other health-related parameters. Trends may be assessed based on any technique known in the art, such as seasonal decomposition of time series (STL). 4) tension on the movable element calculated at least in part based on the range of movement of one or more segments of the movable element; 5) Data obtained from other sensors (e.g., motion sensors, temperature sensors, optical sensors imaging other elements, etc.). 6) Data provided by the element(s) associated with the moving element (e.g., an external system controller). 7) Information about other characteristics of the mobile element, such as identified fractures or shape abnormalities.

[0283] In a simplified example, the health analysis is based solely on the range of movement of the moving elements, and the health assessment and resulting selection of indicators is based on a comparison with two thresholds.

[0284] For example, in a looped moving element: 1) Range of travel <1 mm - The looped moving element is operating within the desired range. The indicator provides information that the looped moving element is in good health. No action is required by the system controller and / or the user. 2) Range of travel within 1-3 mm - The looped moving element is operating at less than the desired tension but within an acceptable range. The indicator provides information that a fault has been detected. No immediate action by the system controller and / or user is required. A predictive maintenance system may provide shorter time-to-maintenance updates. The indicator may also include information about recent trends in the range of travel of the looped moving element. 3) Range of travel > 3 mm - The moving loop element has failed. The indication includes a failure alert that urgent action is required by the system controller and / or the user. The indication may also include the current range of travel of the moving loop element.

[0285] Reference is now made to Table 1, which is a simplified example of a data structure that may be used to select indices for output. [Table 1]

[0286] 2. Model-based evaluation Alternatively or additionally, the assessment of the health of the moving elements and / or the selection of the output indicators is based on a model, which may be developed by any means known in the art.

[0287] In some embodiments, the model used for assessing the health of the moving elements and / or selecting the indicators to be output is a machine learning model trained on a training set by a supervised or unsupervised learning algorithm. Optionally, the model is a neural network.

[0288] Optionally, the training set includes one or more of the following: 1) Images of the moving element or similar moving elements collected during the period of use; 2) Images of the moving element or similar moving elements collected during periods of non-use. 3) Image(s) of the relevant element(s), possibly provided by other optical sensors. 4) Non-image data associated with some or all of the images in the training set.

[0289] For example, non-image data may include the environmental and operating conditions at the time the image was captured.

[0290] Optionally, some or all of the images are tagged with relevant information, such as the range of motion when the image data was captured, whether a fault or malfunction was detected when the image data was captured, the rotational speed of the rotationally moving object, the longitudinal speed of the looped moving element, etc.

[0291] Optionally, the model is trained prior to actual use of the moving element (eg, during a preliminary training period).

[0292] Optionally, the model is trained during a preliminary training period on image data of similar moving elements and / or moving elements in similar systems.

[0293] Optionally, the model is periodically retrained based on image(s) and / or other data collected over time.

[0294] VII. Image data of other components Optionally, the monitoring system also inputs data of other components within the mechanism and / or associated element(s) (e.g., machine / vehicle / aircraft, etc.) to optionally perform additional evaluations as described in PCT Publication WO2022162663, U.S. Provisional Patent Application No. 63 / 394,150, U.S. Provisional Patent Application No. 63 / 521,140, ​​and corresponding PCT applications filed on the same day as this PCT application, which are incorporated herein by reference in their entireties. Images may be provided by optical sensors imaging the moving elements and / or other optical sensors.

[0295] The additional assessment may identify defects and / or failures not necessarily directly related to the movement of the moving elements, such as corrosion, cracks, structural damage, etc. The additional analysis may be combined with the embodiments of the analysis of the movement of the moving elements described herein to provide a more complete health analysis of the mechanism and its components.

[0296] VIII. Methods for Monitoring Moving Elements Reference is now made to Figures 7-8, which are simplified flow diagrams of methods for monitoring a moving element, according to respective embodiments of the present invention. Optional embodiments of inputting image data, determining the range of movement of the moving element, analyzing the range of movement, and generating and outputting an index have been described above.

[0297] 7, image data of at least one section of a moving element is input from at least one optical sensor at 710. A range of motion in a secondary direction is determined at 720. The range of motion is analyzed to determine the health of the moving element at 730. An indicator of the health of the moving element is output at 740.

[0298] Optionally, the range of motion is determined by calculating the maximum amplitude on either side of the contour of the moving element in images captured with an exposure time that exceeds the time expected for the moving element to move through its entire range of motion. As mentioned above, a relatively long exposure time will cause the moving element to appear as a blurred contour.

[0299] Optionally, the method further comprises controlling the optical sensor to capture images of the moving element with an exposure time that exceeds the time expected for the moving element to move through its range of motion.

[0300] Optionally, at 751, the method further includes controlling the moving elements directly and / or by an external controller.

[0301] Optionally, at 752, the method further includes generating maintenance instructions directly and / or by a predictive maintenance system.

[0302] Optionally, at 753, the method further includes displaying information to the user on a user interface. The displayed information includes some or all of the analysis results of the range of movement of the moving element. Further optionally, the displayed information includes an alert that the moving element and / or associated elements require attention (e.g., blocked).

[0303] Optionally, at 754, the method further includes outputting the indicators and / or image data to a machine learning system. The machine learning system may use the provided information to train and / or retrain a model of the moving element.

[0304] 8, image data of at least one section of a moving element is received from at least one optical sensor at 810. A range of movement in a transverse direction is determined at 820. The range of movement is analyzed at 830 to detect one or more aspects related to the health of the moving element.

[0305] Optionally, the aspect is a fault detected in a moving element.

[0306] Optionally, the aspect is a fault detected in a moving element.

[0307] Optionally, the aspect is a trend in the change in range of movement of the movable element (eg, whether the range of movement is increasing slowly or rapidly).

[0308] Optionally, an aspect is prediction of time to failure of a moving or associated element.

[0309] Optionally, the aspect is an identified failure mode. Examples of failure modes include, but are not limited to: 1. Fracture or Breakage: Moving elements can experience fracture or breakage, resulting in complete failure. This can occur due to excessive tension, overload, or material fatigue. 2. Cord or Ply Separation: In looped moving elements with multiple layers or plies, the layers can separate from each other, leading to a loss of strength and effectiveness. 3. Excessive wear: Continuous rubbing against pulleys or other components can cause significant wear on the surface of the looped moving element, leading to thinning and reduced performance. 4. Glazing or Hardening: The surface of a moving element can glaze or harden due to excessive heat or improper tension, reducing its grip and efficiency. 5. Cracking or Aging: Over time, the material of the moving elements may age or deteriorate, leading to cracking and loss of flexibility. 6. Edge wear: The sides of the looped moving element can experience wear resulting in a reduction in width and a reduction in the contact area with the pulley. 7. Material deformation: The material of the moving element may deform under high load or high temperature, affecting its shape and function. 8. Chemical Damage: Exposure to certain chemical or environmental elements can degrade the material of the moving element, weakening it and causing it to fail. 9. Improper Tension: If the looped moving element is under-tensioned or over-tensioned, it can lead to slippage, excessive wear, and premature failure. 10. Foreign Body Injury: Foreign bodies, such as debris or contaminants, can become lodged in the moving elements, causing abrasions or punctures. 11. Misalignment: Misalignment of the looped moving element with the pulley can result in uneven stress distribution and accelerated wear. 12. Improper installation: Incorrect installation techniques, such as the use of incompatible moving elements or pulleys, can lead to premature failure.

[0310] Optionally, an aspect is failure mode propensity.

[0311] Optionally, an aspect is to determine whether a specified failure mode is detected in the moving element.

[0312] At 840, action is taken based on the analysis of the health of the moving elements.

[0313] Optionally, the action is to control the moving element and / or the associated element.

[0314] Optionally, the action is to output an indication of the health of the moving element.

[0315] Optionally, the action is to output an alert of a fault in a moving element.

[0316] Optionally, the action is to output an alert of an anticipated failure in a moving element.

[0317] Optionally, the action is to obtain appropriate operating and / or maintenance instructions for the moving elements having the health aspects determined in 830. Further optionally, the operating and / or maintenance instructions are provided to a user.

[0318] Optionally, the secondary direction of movement is perpendicular to the primary direction of movement, for example, as illustrated in FIG. 5A.

[0319] Optionally, the method further comprises controlling operation of the moving element based on the health of the moving element to prevent operation of the moving element during the fault.

[0320] Optionally, the indicator is output to a controller that controls operation of the moving element based on the health of the moving element to prevent operation of the moving element during the fault.

[0321] Optionally, the indicators are output to a predictive maintenance system that provides maintenance instructions based on the indicators, following the maintenance instructions prevents the fault from escalating into a failure.

[0322] Optionally, the method further comprises displaying an indicator on a user interface to alert a user to the health of the moving element.

[0323] Optionally, the indicators include one or more of the following: ·Maintenance instructions; · Time to failure estimation; Fault alerts; and -Operation instructions in response to detected faults.

[0324] Optionally, a larger range of movement is assessed to indicate a lower tension in the movable element compared to the tension at a smaller range of movement.

[0325] Optionally, the analysis includes assessing the health of the moving element by comparing the magnitude of the range of motion to at least one threshold value.

[0326] Optionally, the moving element is a looped moving element, and calculating the maximum amplitude includes calculating the distance between two sides of a blurred outline of the looped moving element visible in the image at a position having a maximum difference between the two sides.

[0327] Optionally, the range of motion is determined based on a statistical analysis of a series of image frames.

[0328] Optionally, the image data comprises a video sequence of images.

[0329] Optionally, the method further comprises assessing the health of the moving element based on changes in magnitude of the range of motion over time.

[0330] Optionally, the method further comprises assessing the health of the moving element based on the shape of the at least one segment.

[0331] Optionally, the method further comprises assessing the health of the moving element based on changes in the shape of the moving element in multiple sections of the moving element.

[0332] Optionally, the image data includes images provided by a plurality of optical sensors, each capturing an image of a respective section of the moving element. Alternatively, the image data is provided by a single optical sensor in a fixed position relative to the moving element.

[0333] Optionally, at least one imaged segment of the movable element is at least 1%, 5%, 10%, 25%, or 50% of the length of the movable element. Further optionally, at least one of the segments is between 1% and 10%, between 10% and 20%, or between 1% and 50% of the total length of the movable element.

[0334] Optionally, the moving element is a loop moving element. Further optionally, the loop moving element is one of: Pulley belt; ·cable; ·strap; rope; and ·chain.

[0335] Optionally, the looped movable element is wrapped around at least two pulleys.

[0336] Alternatively, the moving element is a rotary moving element.

[0337] Optionally, the indication is obtained from a data structure indexed by at least one parameter determinable from the movement range, and optionally other parameters.

[0338] Optionally, the analysis is based on a machine learning model trained using a training set of images collected during operation of at least one of the moving elements and similar moving elements.

[0339] Optionally, the machine learning model is a neural network.

[0340] Optionally, the machine learning model is trained using a supervised or unsupervised learning algorithm.

[0341] Optionally, the analysis includes predicting the future health of the moving element by performing a trend analysis on changes in the range of motion over time.

[0342] IX. Illustrative Embodiments According to some embodiments of the present invention, an exemplary method is provided for monitoring the status and / or integrity of operation of a moving and / or rotating element (for purposes of this example, designated as a moving element).

[0343] Optionally, the movable element is a loop movable element having a primary longitudinal movement. Alternatively, the movable element is a rotary movable element having a primary rotational movement.

[0344] The system includes at least one optical sensor and a processor. The at least one optical sensor is configured to be fixed on, proximate to, and / or within view of the movable element and configured to capture multiple images of the movable element while it is moving. The processor is executable to receive the multiple images captured from the at least one optical sensor and calculate a maximum amplitude of secondary movement of the movable element along an axis perpendicular to the axis of motion and / or axial and radial movements of the movable element and / or combinations thereof, and if the maximum amplitude of secondary movement and / or displacement of the movable element exceeds a predefined threshold, the processor is executable to output a signal indicative of a fault in or associated with the movable element.

[0345] Optionally, the movable element is fixed at at least one end thereof to an element that is stationary relative to movement of the movable element.

[0346] Further optionally, the moving element is a looped moving element and the element stationary relative to the movement of the looped moving element is a pulley.

[0347] In one example, the looped moving element is a pulley belt and the stationary element is the pulley around which it is wrapped. The linkage allows longitudinal movement of the pulley belt.

[0348] Alternatively, the moving element is a rotary moving element. The stationary element depends on the type of rotary moving element.

[0349] In the first example, the moving element is a rotor that rotates about an axis and the stationary element is a stator that is stationary relative to the frame of the machine of which it is a component.

[0350] In a second example, the movable element is a rotating rod or shaft and the stationary element is a rotating plate attached to the end of the rod and causing it to rotate.

[0351] In a third example, the moving element is a rotating shaft that rotates about its axis, and the stationary element is a bearing.

[0352] In a fourth example, the moving element is a turbine rotor and the stationary element is a turbine casing.

[0353] In a fifth example, the moving element is an impeller and the stationary element is a turbo housing.

[0354] According to some embodiments, the system may be a system for monitoring the tension and / or tension of a looped moving element (e.g., a belt, chain, or band) connected between two elements, such as, for example, a timing belt connected between two pulleys, with the monitoring occurring in real time while the looped moving element is moving.

[0355] According to some embodiments, the system may be a system for monitoring strain in a rotating moving element (e.g., a shaft or turbine blades) coupled between two elements, such as, for example, a timing belt coupled between two pulleys, with the monitoring occurring in real time while the rotating moving element is moving.

[0356] The system may be configured to provide an indication of the integrity of the moving element. Alternatively, or additionally, the system may be configured to provide an indication of a potential fault in the moving element or in its function.

[0357] According to some embodiments, the system may be configured to receive a signal, such as an image and / or image data, from at least one optical sensor disposed on or near the moving element. According to some embodiments, the system may be configured to identify at least one change in the received signal. According to some embodiments, for an identified change in the received signal, the system applies the at least one identified change to an algorithm configured to analyze the identified change in the received signal and classify whether the identified change in the received signal is associated with a failure mode of the moving element, thereby labeling the identified change as a fault based, at least in part, on the acquired data associated with the failure mode of the moving element. According to some embodiments, for an identified change classified as associated with a failure mode, the system may output a signal indicative of the identified change associated with the failure mode.

[0358] According to some embodiments, the system may be configured to generate at least one model of a trend in the identified disorder, where the trend may include a rate of change in the disorder.

[0359] According to some embodiments, the system may be configured to prevent failure of moving elements by identifying faults in real time and monitoring changes in the faults in real time.

[0360] Reference is made to FIG. 9, which is a schematic illustration of a system for monitoring potential faults in a moving element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the element, according to some exemplary embodiments of the present invention.

[0361] According to some embodiments, a system 900 for monitoring for potential faults in an element may include at least one optical sensor 912 configured to be fixed on or near a moving element. According to some embodiments, the system 900 may be configured to monitor the moving element in real time. According to some embodiments, the system 900 may include at least one processor 902 in communication with one optical sensor 912. According to some embodiments, the processor 902 may be configured to receive signals (e.g., image data, images, other data, etc.) from the one or more optical sensors 912. According to some embodiments, the processor 902 may include an embedded processor, a cloud computing system, or any combination thereof. According to some embodiments, the processor 902 may be configured to process signals received from the one or more optical sensors 912 (also referred to herein as received signals or received data). According to some embodiments, the processor 902 may include an image processing module 906 configured to process signals received from the one or more optical sensors 912.

[0362] According to some embodiments, one or more optical sensors 912 may be configured to detect light reflected from the surface of the moving element. This can be advantageous because surfaces with different textures reflect light differently. For example, a polished surface may have a flat surface and reflect a large proportion of parallel light rays, while a matte surface may reflect more light than a non-smooth surface, scattering (diffusing) light evenly in all directions. A polished surface may be smooth and shiny, absorbing very little light, and may reflect more light, thereby allowing images detected from light reflected from a polished surface to be sharper than images detected from light reflected from a matte surface. Thus, the surface texture of a crack, nick, or any other surface defect may differ from the undamaged surface surrounding it (or, in other words, the original baseline surface), and therefore, different light reflection from the surface allows for the detection of small defects. Furthermore, this phenomenon can be enhanced by changing the wavelength, intensity, and / or direction of the light. According to some embodiments, and as described in further detail elsewhere herein, the system may include one or more light sources (also referred to herein as illumination light sources) configured to illuminate the moving elements.

[0363] According to some embodiments, changing the direction of the light may include moving the light source. According to some embodiments, changing the direction of the light may include maintaining two or more fixed light source positions while powering (or otherwise operating) the light sources at different times, thereby changing the direction of the light illuminating the movable element. According to some embodiments, and as described in more detail elsewhere herein, the system may include one or more light sources positioned such that their movement illuminates the movable element. According to some embodiments, the system may include multiple light sources, each positioned at a different position relative to the movable element.

[0364] According to some embodiments, the wavelength, intensity, and / or direction of the one or more light sources may be controlled by a processor. According to some embodiments, varying the wavelength, intensity, and / or direction of the one or more light sources thereby enables detection of surface defects on the surface of the moving element. According to some embodiments, the one or more optical sensors 912 may enable detection of minute dents and / or defects, e.g., two to three tenths of a millimeter, that may not be visible to the naked eye, by analyzing the reflected light.

[0365] According to some embodiments, the one or more optical sensors 912 may include a camera. According to some embodiments, the one or more optical sensors 912 may include an electro-optical sensor. According to some embodiments, the one or more optical sensors 912 may include any one or more of a charge-coupled device (CCD) and a complementary metal-oxide semiconductor (CMOS) sensor (or active pixel sensor), or any combination thereof. According to some embodiments, the one or more optical sensors 912 may include any one or more of a point sensor, a distributed sensor, an external sensor, an internal sensor, a transmissive sensor, a diffuse reflective sensor, a retro-reflective sensor, or any combination thereof.

[0366] According to some embodiments, the one or more optical sensors may include one or more lenses and / or fiber optic sensors. According to some embodiments, the one or more optical sensors may include a software correction matrix configured to generate an image from the received data. According to some embodiments, the one or more optical sensors may include a focus sensor configured to enable the optical sensor to detect changes in the acquired data. According to some embodiments, the focus sensor may be configured to enable the optical sensor to detect changes in one or more pixels of the acquired signal.

[0367] According to some embodiments, the system 900 may include one or more user interface modules 914 in communication with the processor 902. According to some embodiments, the user interface module 914 may be configured for receiving data from a user, the data being associated with any one or more of a moving element, a type of moving element, a type in which the moving element is used, one or more environmental parameters, one or more failure modes of the moving element, or any combination thereof. According to some embodiments, the user interface module 914 may include any one or more of a keyboard, a display, a touchscreen, a mouse, one or more buttons, or any combination thereof. According to some embodiments, the user interface module 914 may include a configuration file that may be automatically and / or manually generated by a user. According to some embodiments, the configuration file may be configured to identify at least one segment. According to some embodiments, the configuration file may be configured to allow a user to mark and / or select at least one segment.

[0368] According to some embodiments, the system 900 may include a storage module 904 configured to store data and / or instructions (e.g., code) for execution by the processor 902. According to some embodiments, the storage module 904 may be in communication (or operative communication) with the processor 902. According to some embodiments, the storage module 904 may include a database 908 configured to store data associated with any one or more of the system 900, moving elements, user input data, one or more training sets (or data sets used to train one or more of the algorithms), or any combination thereof. According to some embodiments, the storage module 904 may include one or more algorithms 910 (optionally embodied as computer code) stored thereon and configured to be executed by the processor 902. According to some embodiments, the one or more algorithms 910 may be configured to analyze and / or classify received signals, as described in more detail elsewhere herein. According to some embodiments, and as described in more detail elsewhere herein, the one or more algorithms 910 may include one or more preprocessing techniques for preprocessing received signals.

[0369] According to some embodiments, the one or more algorithms 910 may include a change detection algorithm configured to identify a change in the received signal. According to some embodiments, the one or more algorithms 910 and / or change detection algorithm may be configured to receive signals from one or more optical sensors 912 to obtain data associated with at least one failure mode characteristic of the moving element and / or identify at least one change in the received signal.

[0370] According to some embodiments, the one or more algorithms 910 may include a classification algorithm configured to classify the identified change. According to some embodiments, the classification algorithm may be configured to classify the identified change as a fault. According to some embodiments, the classification algorithm may be configured to classify the identified change as normal operation (or movement) of the moving element.

[0371] According to some embodiments, one or more algorithms 910 may be configured to analyze the fault (or the identified change classified as a fault). According to some embodiments, one or more algorithms 910 may be configured to output a signal (or alarm) indicative of the identified change associated with a failure mode.

[0372] According to some embodiments, one or more algorithms 910 may be configured with the processor 902 to perform a method for monitoring for potential faults in a moving element, such as the method illustrated in FIG. 10.

[0373] Reference is now made to Fig. 10, which is a simplified flow chart of a computer-implemented method for monitoring potential faults in a moving element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the moving element, according to some exemplary embodiments of the present invention, and Fig. 11, which is a simplified schematic block diagram of a method for monitoring potential faults in a moving element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the moving element, according to some exemplary embodiments of the present invention. According to some embodiments, method 1000 of Fig. 10 may include one or more steps of block diagram 1100 of Fig. 11.

[0374] According to some embodiments, at step 1002, the method may include receiving a signal from at least one optical sensor. According to some embodiments, at step 1004, the method may include identifying at least one change in the received signal. According to some embodiments, at step 1006, the method may include analyzing the identified change in the received signal and classifying whether the identified change in the received signal is associated with a failure mode of the moving element, thereby labeling the identified change as a fault. According to some embodiments, at step 1008, the method may include outputting a signal indicative of the identified change associated with the failure mode. According to some embodiments, at step 1010, the method may include generating at least one model of a trend in the identified fault. According to some embodiments, at step 1012, the method may include alerting a user of a predicted failure based, at least in part, on the generated model.

[0375] According to some embodiments, such as that shown in FIG. 11 , the method may include signal acquisition 1102, i.e., receiving one or more signals. According to some embodiments, the method may include receiving the one or more signals from at least one optical sensor fixed on or near a moving element, such as one or more sensors 912 of system 900. According to some embodiments, the one or more signals may include one or more images. According to some embodiments, the one or more signals may include one or more portions of an image. According to some embodiments, the one or more signals may include a set of images, such as a packet of images. According to some embodiments, the one or more signals may include one or more videos.

[0376] According to some embodiments, the method may include preprocessing (1104) one or more signals. According to some embodiments, the preprocessing may include converting the one or more signals into electronic signals (e.g., from optical signals to electrical signals). According to some embodiments, the preprocessing may include generating one or more images, one or more sets of images, and / or one or more videos from the one or more signals. According to some embodiments, the preprocessing may include dividing the one or more images, one or more portions of one or more images, one or more sets of images, and / or one or more videos into multiple tiles. According to some embodiments, the preprocessing may include applying one or more filters to the one or more images, one or more portions of one or more images, one or more sets of images, one or more videos, and / or multiple tiles. According to some embodiments, the one or more filters may include one or more noise reduction filters.

[0377] According to some embodiments, the method may include stitching together multiple signals obtained from two or more optical sensors. According to some embodiments, the method may include stitching together multiple signals in real time.

[0378] According to some embodiments, the method may include identifying at least one segment within any one or more of the received signal, one or more images, one or more portions of one or more images, one or more sets of images, and / or one or more videos. According to some embodiments, the method may include monitoring the (identified) at least one segment. According to some embodiments, the at least one change in the signal is a change in at least one segment. According to some embodiments, the at least one change in the one or more images, one or more portions of one or more images, one or more sets of images, and / or one or more videos is a change in at least one segment.

[0379] According to some embodiments, a user may mark a segment to be monitored for an image and / or a portion of an image and / or at least a portion of a video. According to some embodiments, a user may input a location to be monitored. According to some embodiments, an algorithm may be configured to identify at least one segment within the user-input location.

[0380] According to some embodiments, the method may include applying one or more signals, one or more images, one or more portions of one or more images, one or more sets of images, and / or one or more videos to a change detection algorithm 1108 (e.g., one or more algorithms 910 of system 900, etc.) configured to detect changes therein. According to some embodiments, the change detection algorithm may include one or more machine learning models 1122.

[0381] According to some embodiments, the method may include detecting whether there is a change in the shape of at least one segment, the size of at least one segment, the rate of occurrence of at least one segment, or any combination thereof, within the received signal. According to some embodiments, the method may include detecting whether there is a change in the shape, size, and / or rate of occurrence of at least one segment over time. According to some embodiments, the method may include detecting whether there is a change in the shape, size, and / or rate of occurrence of at least one segment over a specified period of time, such as one second, several seconds, one minute, one hour, one day, one week, several weeks, or any range therebetween.

[0382] According to some embodiments, at least one segment may include a potential fault that needs to be monitored, such as, for example, a surface defect. According to some embodiments, at least one segment may include an outline of a by-product of a moving element, such as, for example, an igniting spark. According to some embodiments, at least one segment may include a boundary of a surface defect. According to some embodiments, at least one segment may include at least one boundary of a puddle perimeter, a droplet perimeter, an impregnated area (or material) perimeter, or any combination thereof. According to some embodiments, at least one segment may include a boundary of a spark.

[0383] According to some embodiments, at least one segment may include a boundary of a particular element of the moving element. According to some embodiments, the method may include identifying the geometry of the at least one segment as the particular element of the moving element. According to some embodiments, the method (or identification of the geometry) may include analyzing any one or more of total intensity, variance intensity, spackle detection, line segment detection, line segment registration, edge segment curvature estimation, homology estimation, specific object identification, object detection, semantic segmentation, background model, change detection, optical flow detection, or reflectance detection, fire detection, or any combination thereof.

[0384] According to some embodiments, the method may include obtaining data associated with at least one failure mode characteristic of the moving element, or failure mode identification 1106. According to some embodiments, the data associated with the at least one failure mode characteristic of the moving element may include a type of failure mode. According to some embodiments, the data associated with the at least one failure mode characteristic of the moving element may include a location or range of locations of the failure mode on the moving element and / or a particular type of failure mode.

[0385] According to some embodiments, the failure mode may include one or more aspects that may cause a failure in the moving element. According to some embodiments, and as described in more detail elsewhere herein, the failure mode may include a significant progression of an identified fault. According to some embodiments, the failure mode may include any one or more of a change in dimension, a change in position, a change in color, a change in texture, a change in size, a change in appearance, a fracture, structural damage, a tear, a fracture size, a critical fracture size, a fracture location, a fracture propagation, a specified pressure applied to the moving element, a change in the movement of one component relative to another component, a defect diameter, a cut, warping, expansion, deformation, delamination, abrasion, corrosion, oxidation, sparks, smoke, a change in color / hue, a change in dimension, a change in position, a change in color, a change in size, a change in appearance, or any combination thereof.

[0386] According to some embodiments, the method may include obtaining data associated with the characteristics of at least one failure mode of the moving element by receiving user input. According to some embodiments, the method may include obtaining data associated with the characteristics of at least one failure mode of the moving element by analyzing the received signals and detecting at least one segment associated with the failure mode. According to some embodiments, the method may include obtaining data associated with the characteristics of at least one failure mode of the moving element by analyzing the received signals and detecting potential failure modes. According to some embodiments, the method may include obtaining data associated with the characteristics of at least one failure mode of the moving element by analyzing the received signals and detecting one or more previously unknown failure modes.

[0387] According to some embodiments, obtaining data associated with the characteristic of at least one failure mode of the moving element includes receiving data input from a user. According to some embodiments, the user may input the data associated with the failure mode of the moving element using the user interface module 914. According to some embodiments, the method may include monitoring the moving element based, at least in part, on the received user-input data. According to some embodiments, the user may input a type of failure mode of the moving element. According to some embodiments, the user may input a type of failure mode associated with a particular identified segment. According to some embodiments, the user may input a location of the failure mode. According to some embodiments, the user may identify one or more of the at least one segment as being prone to failure and / or in a location where failure is likely to occur.

[0388] According to some embodiments, the method may include automatically obtaining data associated with the characteristics of at least one failure mode of the moving element. According to some embodiments, the method may include obtaining data associated with the characteristics of at least one failure mode of the moving element without user input. According to some embodiments, the method may include analyzing the received signal to automatically obtain data from a database, such as database 908. According to some embodiments, one or more algorithms 910 may be configured to identify one or more failure modes in the database, which may be associated with identified segments of the received signal of the moving element. According to some embodiments, the method may include searching the database for possible failure modes of the identified segments. According to some embodiments, the method may include obtaining data from the database, the data associated with possible failure modes of the identified segments.

[0389] According to some embodiments, the method may include obtaining data associated with characteristics of at least one failure mode of the moving element by identifying a previously unknown failure mode. According to some embodiments, identifying the previously unknown failure mode may include applying the received signal and / or the identified segment to a machine learning algorithm 1124 configured to determine a failure mode of the moving element. According to some embodiments, the machine learning algorithm 1124 may be trained to identify potential failure modes of the identified segment.

[0390] 10, the method may include identifying at least one change in the received signal and / or at least one identified segment. According to some embodiments, the method may include applying the received signal and / or at least one identified segment to a change detection algorithm, such as, for example, change detection algorithm 1108, configured to detect (or identify) at least one change in the received signal and / or at least one identified segment.

[0391] According to some embodiments, identifying at least one change in the signal includes identifying a change in the rate of change in the signal. For example, the algorithm may be configured to identify periodically occurring changes in the analyzed signal, and then the analyzed signal may "return" to a previous state (e.g., prior to the change in the analyzed signal). According to some embodiments, the algorithm may be configured to identify a change in the rate of occurrence of the identified change.

[0392] According to some embodiments, the term "analyzed signal," as used herein, may describe any one or more of the received signals, such as raw signals from one or more optical sensors, processed or preprocessed signals from one or more optical sensors, one or more images, one or more packets of images, one or more portions of one or more images, one or more videos, one or more portions of one or more videos, at least one identified segment, at least a portion of an identified segment, or any combination thereof. According to some embodiments, identifying at least one change in the analyzed signal may include analyzing raw data of the received signal.

[0393] According to some embodiments, the change detection algorithms 1108 may include any one or more of binary change detection, quantitative change detection, and qualitative change detection.

[0394] According to some embodiments, binary change detection may include an algorithm configured to classify an analyzed signal as having a change or not having a change. According to some embodiments, binary change detection may include an algorithm configured to compare two or more analyzed signals. According to some embodiments, for comparisons indicating that the compared analyzed signals are the same or essentially the same, the classifier labels the analyzed signal as having no detected (or identified) change. According to some embodiments, for comparisons indicating that the compared analyzed signals are different, the classifier labels the analyzed signal as having a detected (or identified) change. According to some embodiments, two or more analyzed signals that are different may have at least one pixel that is different. According to some embodiments, two or more analyzed signals that are the same may have identical characteristics and / or pixels. According to some embodiments, the algorithm may be configured to set a threshold number of different pixels above which two analyzed signals may be considered different.

[0395] Advantageously, the change detection algorithm 1108 allows for rapid detection of changes in the analyzed signal and can be very sensitive to even the slightest changes therein. Even more so, binary change detection detection and alerting can occur within a single signal, e.g., within a few milliseconds depending on the signal output rate of an optical sensor, or for optical sensors including cameras, within a single image frame, e.g., within a few milliseconds depending on the frame rate of the camera.

[0396] According to some embodiments, a binary change detection algorithm may, for example, analyze the analyzed signal to determine whether non-black pixels change to black over time, thereby indicating a possible change in the position of the moving element, perhaps due to deformation or due to a change in the position of another component associated with the moving element. According to some embodiments, if the binary change detection algorithm detects a change in the signal, a warning signal (or alarm) may be generated to alert the device or a technician that maintenance may be required.

[0397] According to some embodiments, the binary change detection algorithm may be configured to determine the cause of the identified change using one or more machine learning models. According to some embodiments, the method may include determining the cause of the identified change by applying the identified cause to a machine learning algorithm. For example, for a black pixel that may change to a color other than black over time (or through continuous analyzed signals), the machine learning algorithm may output that the change indicates a change in the material of the moving element, for example, due to overheating. According to some embodiments, the method may include generating a signal, such as an informational signal or a warning signal, if necessary. According to some embodiments, the warning signal may be a one-time signal or a continuous signal that may require some form of action to reset the warning signal, for example.

[0398] According to some embodiments, the method may include identifying at least one change in the signal by analyzing dynamic motion of the movable element, which according to some embodiments may include any one or more of linear motion, rotational motion, cyclic (repetitive) motion, arcuate motion, or any combination thereof.

[0399] According to some embodiments, change detection may include quantitative change detection. According to some embodiments, quantitative change detection may include an algorithm configured to determine whether a change in magnitude above a certain threshold has occurred in the analyzed signal. According to some embodiments, a change in magnitude above a certain threshold may include a cumulative change in magnitude regardless of time, and / or a rate of change (or rates of change) in magnitude. For example, a value reflecting a change in magnitude may represent the number of pixels that have changed, the percentage of pixels that have changed, the total difference in the numerical values ​​of one or more pixels within the field of view (or analyzed signal), combinations thereof, and the like. According to some embodiments, the quantitative change detection algorithm may output quantitative data associated with the change in the analyzed signal.

[0400] According to some embodiments, the change detection may include a qualitative change detection algorithm. According to some embodiments, the qualitative change detection algorithm may include an algorithm configured to classify the analyzed signal as indicative of a change in the moving element. According to some embodiments, the qualitative change detection algorithm may include a machine learning model configured to receive the analyzed signal and classify the analyzed signal into categories including at least: including a change in behavior of the moving element, and not including a change in behavior of the moving element.

[0401] According to some embodiments, the change detection algorithm may be configured to analyze other, more complex changes in the analyzed signal generated by the optical sensor with the assistance of a machine learning model. According to some embodiments, the machine learning model may be trained to recognize complex and diverse changes. According to some embodiments, the machine learning model may be able to identify complex changes, such as for a signal generated by an optical sensor that may begin to exhibit some periodic instability, such that the signal appears normal for a period of time and then appears abnormal for a period of time before appearing normal again. The signal may then exhibit some similar, but different, anomaly, and the change detection algorithm may be configured to analyze the changes and train itself to detect probable causes of the anomaly over time. According to some embodiments, the change detection algorithm may be configured to generate a warning or information signal, if necessary, to alert a user to the change in the moving element.

[0402] Reference is made to FIG. 12, which is a schematic block diagram of a system for monitoring potential faults in a moving element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the moving element, in accordance with some exemplary embodiments of the present invention.

[0403] As shown in the exemplary system of FIG. 12 , an optical sensor may receive one or more signals from a moving element, such as, for example, moving element 1202. According to some embodiments, the optical sensor may generate a signal, such as, for example, an image or video, and transmit the generated signal to image processing module 1206. According to some embodiments, the image processing module processes the signal generated by the optical sensor (or image sensor 1204 of FIG. 12 ) so that the data can be analyzed by data analysis module 1218 (or algorithm 910 as described herein). According to some embodiments, image processing module 1206 may include any one or more of an image / frame acquisition module 1208, a frame rate control module 1210, an exposure control module 1212, a noise reduction module 1214, a color correction module 1216, and the like. According to some embodiments, the data analysis module (or algorithm 910 as described herein) may include a change detection algorithm, such as, for example, change detection algorithm 1108. According to some embodiments, the user interface module 1232 (described below) may issue any warning signals resulting from the signal analysis performed by the algorithm. According to some embodiments, any one or more of the signals and / or algorithms may be stored on cloud storage. According to some embodiments, the processor may be located on the cloud, e.g., cloud computing, which may be co-located with the embedded processor.

[0404] According to some embodiments, the data analysis module 1218 may include any one or more of a binary (visual) change detector 1220 (or a binary change detection algorithm as described in more detail elsewhere herein), a quantitative (visual) change detector 1222 (or a quantitative change detection algorithm as described in more detail elsewhere herein), and / or a qualitative (visual) change detector 1224 (or a qualitative change detection algorithm as described in more detail elsewhere herein). According to some embodiments, the qualitative (visual) change detector 1224 may include any one or more of an edge detector 1226 and / or a shape (deformation) detector 1228. According to some embodiments, the data analysis module 1218 may include and / or be in communication with a user interface module 1232. According to some embodiments, and as described in more detail elsewhere herein, the user interface module 1232 may include a monitor 1234. According to some embodiments, the user interface module 1232 may be configured to output alarms and / or notifications 1236 / 1126.

[0405] According to some embodiments, a change detection algorithm, such as, for example, change detection algorithm 1108, may be implemented on an embedded processor or a processor near the optical sensor. Thus, a change detection algorithm, such as, for example, change detection algorithm 1108, may enable rapid detection and avoid time lags associated with transmitting data to a remote server (such as the cloud).

[0406] According to some embodiments, once a change is identified using a change detection algorithm, the identified change may be classified using a classification algorithm. According to some embodiments, in step 1006, the method may include analyzing the identified change in the received signal (or analyzed signal) and classifying whether the identified change in the received signal (or analyzed signal) is associated with a failure mode of the moving element, thereby labeling the identified change as a fault. According to some embodiments, the method may include applying the received signal (or analyzed signal) to an algorithm configured to analyze the identified change in the received signal and classify whether the identified change in the received signal is associated with a failure mode of the moving element based, at least in part, on the acquired data.

[0407] According to some embodiments, the method may include applying the identified change to an algorithm configured to perform a match between the identified change and the acquired data associated with a failure mode. According to some embodiments, the algorithm may be configured to determine whether the identified change may potentially develop into one or more failure modes. According to some embodiments, the algorithm may be configured to determine whether the identified change may potentially develop into one or more failure modes based at least in part on the acquired data. According to some embodiments, the method may include labeling the identified change as a fault if the algorithm determines that the identified change may potentially develop into one or more failure modes.

[0408] For example, an identified change in a surface defect and / or discontinuity may be identified as a fault once the discontinuity or defect reaches a certain size or length and may be associated with a failure mode that is a critical discontinuity size or critical defect size.

[0409] For example, if the identified change involves the texture or color of the moving element, the fault may be identified as corrosion, and the failure mode may be the amount of corrosion or the depth of corrosion within the moving element.

[0410] According to some embodiments, the fault may include any one or more of structural damage, a cut, a defect, a predetermined cut size and / or length, a cut growth rate, a cut propagation, a crack, a defect diameter, warping, expansion, deformation, delamination, wear, corrosion, oxidation, sparks, smoke, fluid flow rate, drop formation, drop size, fluid or drop volume, drop formation rate, fluid accumulation rate, texture change, color / hue change, size of formed bubbles, puddle formation, puddle propagation, a change in dimension of at least a portion of a segment, a change in position of at least a portion of a segment, a change in color of at least a portion of a segment, a change in texture of at least a portion of a segment, a change in size of at least a portion of a segment, a change in appearance of at least a portion of a segment, linear motion of at least a portion of a segment, rotational motion of at least a portion of a segment, cyclic (repetitive) motion of at least a portion of a segment, a change in speed of motion of at least a portion of a segment, under-lubrication, over-lubrication, a diameter change, signs of delamination, signs of wear, improper alignment, groove problems, or any combination thereof.

[0411] According to some embodiments, the algorithm may identify faults using one or more machine learning models. According to some embodiments, and as described in more detail elsewhere herein, the machine learning models may be trained over time to identify one or more faults. According to some embodiments, the machine learning models may be trained to identify previously unknown faults by analyzing baseline behavior of the moving elements.

[0412] Advantageously, using a machine learning model to identify faults allows for the detection of different types of faults, or even similar faults that may appear differently in different machines or situations, or even at different angles of the optical sensor. Thus, the machine learning model may increase the sensitivity of the detection of one or more faults.

[0413] According to some embodiments, the system and / or one or more algorithms may include one or more suppressor algorithms 1110 (also referred to herein as suppressors 1110). According to some embodiments, the one or more suppressor algorithms may be configured to classify whether a detected fault may escalate into a failure, such as illustrated by failure mode bifurcation point 1112 in FIG. 11 . According to some embodiments, the one or more suppressor algorithms 1110 may include one or more machine learning models 1120. According to some embodiments, the one or more suppressor algorithms 1110 may classify the fault and / or the propagation of the fault as benign.

[0414] According to some embodiments, in step 1008, for an identified fault, the method may include outputting a signal, such as a warning signal, indicative of the identified change associated with the failure mode. According to some embodiments, the method may include storing the identified change in a database, thereby augmenting the data set for training one or more machine learning models.

[0415] According to some embodiments, the method may include labeling data associated with any one or more of the classifications as indicated by failure mode identification 1106, change detection algorithm 1108, suppressor 1110, and failure mode nodal point 1112. According to some embodiments, the method may include supervised labeling 1116, such as manual labeling of data using user input (or expert knowledge).

[0416] According to some embodiments, if an identified change is classified as not associated with a failure mode (such as that indicated by arrow 1150 in FIG. 11 ), it may be identified (or classified) as normal, or in other words, as normal behavior or operation of the moving element. According to some embodiments, for an identified change classified as normal, the method may include storing data associated with the identified change, thereby adding the identified change to a database to augment the data set for training 1118 one or more machine learning models (e.g., one or more machine learning models 1120 / 1122 / 1124). According to some embodiments, the method may include using the data associated with the identified change for further investigation, where the further investigation includes at least one of adding a failure mode, updating an algorithm configured to identify the change, and training the algorithm to ignore the identified change in the future, thereby improving the algorithm configured to identify the change.

[0417] According to some embodiments, if the identified change is classified as associated with a failure mode (such as indicated by arrow 1155 in FIG. 11 ), the method may include trend analysis and failure prediction 1114. According to some embodiments, at step 1010, the method may include generating at least one model of a trend in the identified faults. According to some embodiments, the method may include generating at least one model of a trend based on the plurality of analyzed signals. According to some embodiments, the method may include generating at least one model of a trend by calculating the evolution of the identified change in the analyzed signals over time. According to some embodiments, the trend may include a rate of change of the faults. According to some embodiments, the method may include generating at least one model of a trend in the identified faults by calculating a correlation of the rate of change of the faults with one or more environmental parameters. According to some embodiments, the one or more environmental parameters may include any one or more of temperature, season or time of year, barometric pressure, time of day, operating hours of the moving element, operational duration of the moving element, identified user of the moving element, operational mode of the moving element, or any combination thereof.

[0418] According to some embodiments, the operating mode of the moving element may include any one or more of a frequency of movement, a speed of movement, a power consumption during operation, a change in power consumption during operation, and the like. According to some embodiments, generating a model of at least one trend in the identified fault by calculating a correlation of a rate of change of the fault with one or more environmental parameters may include taking into account different influences in the surroundings of the moving element. According to some embodiments, the method may include mapping different environmental parameters that affect the operation of the moving element, where the environmental parameters may change over time.

[0419] According to some embodiments, at step 1012, the method may include alerting a user of the predicted failure based at least in part on the generated model. According to some embodiments, the method may include outputting a notice and / or alert 1126 to the user. According to some embodiments, the method may include alerting a user of the predicted failure. According to some embodiments, the method may include alerting a user of the predicted failure by outputting any one or more of the predicted failure time (or time range) and failure mode characteristics, or any combination thereof. According to some embodiments, the method may include outputting a prediction of when the identified fault is likely to result in a failure of the moving element based at least in part on the generated model. According to some embodiments, predicting when a failure is likely to occur at the moving element may be based at least in part on expected future environmental parameters. According to some embodiments, predicting when a failure is likely to occur at the moving element may be based at least in part on a known schedule, such as, for example, a calendar.

[0420] According to some embodiments, a system for monitoring potential faults in a moving element, such as system 900, may include one or more light sources configured to illuminate at least a portion of a vicinity of the moving element. According to some embodiments, the one or more light sources may include any one or more of a light bulb, a light emitting diode (LED), a laser, a fiber optic light source, a fiber optic cable, and the like. According to some embodiments, a user may input the location (or location) of the light source, the direction of illumination of the light source (or in other words, the direction the light is pointed), the duration of illumination, the wavelength, the intensity, and / or the frequency of illumination of the light source associated with one or more optical sensors. According to some embodiments, one or more algorithms may be configured to automatically position one or more light sources. According to some embodiments, one or more algorithms may direct the operating mode of one or more light sources. According to some embodiments, the one or more algorithms may direct and / or manipulate any one or more of the illumination intensity of one or more light sources, the number of powered light sources, the location of the powered light sources, and the wavelength, intensity, and / or frequency of illumination of one or more light sources, or any combination thereof.

[0421] Advantageously, an algorithm configured to direct and / or operate one or more light sources may increase the clarity of the received signal by reducing dark areas (e.g., areas from which light is not reflected and / or areas that were not illuminated) and may modify (or optimize) the color saturation of the received signal (or image).

[0422] According to some embodiments, the one or more algorithms may be configured to detect and / or calculate the position, duration, wavelength, intensity, and / or frequency of illumination of the one or more light sources relative to the one or more optical sensors. According to some embodiments, the one or more algorithms may be configured to detect and / or calculate the position, duration, wavelength, intensity, and / or frequency of illumination of the one or more light sources relative to the one or more optical sensors based, at least in part, on the analyzed signals. According to some embodiments, the processor may control operation of the one or more light sources. According to some embodiments, the processor may control any one or more of the duration, wavelength, intensity, and / or frequency of illumination of the one or more light sources.

[0423] According to some embodiments, the method may include obtaining a position, duration, wavelength, intensity, and / or frequency of illumination of one or more light sources relative to one or more optical sensors. According to some embodiments, the method may include obtaining the position of the one or more light sources via any one or more of user input, detection, and / or using one or more algorithms. According to some embodiments, the method may include classifying whether an identified change in the (analyzed) signal is associated with a failure mode of the moving element based, at least in part, on any one or more of the position(s), duration, wavelength, intensity, and frequency of illumination of the at least one light source.

[0424] According to some embodiments, the method may include outputting data associated with optimal locations for placement (or locations) of the optical sensors, from which potential failure modes can be detected. According to some embodiments, the one or more algorithms may be configured to calculate at least one optimal location for placement (or location) of the one or more optical sensors based, at least in part, on the acquired data, data stored in the database, and / or user-entered data.

[0425] According to some embodiments, the light source may illuminate the moving element with one or more wavelengths from a broad spectral range, both visible and invisible. According to some embodiments, the light source may include a strobe, or a light source configured to illuminate with short pulses. According to some embodiments, the light source may be configured to emit strobe light without the use of a global shutter sensor.

[0426] According to some embodiments, the wavelength may include any one or more light sources in the ultraviolet region, the infrared region, or a combination thereof. According to some embodiments, one or more light sources may be mobile or movable. According to some embodiments, one or more light sources may change output wavelength during operation, change illumination direction during operation, change one or more lenses, and the like. According to some embodiments, the light source may be configured to change illumination using one or more optical fibers (FOs), for example, by using different fibers to generate light at different times or by combining two or more fibers simultaneously. According to some embodiments, the optical fiber may include one or more light sources, such as, for example, LEDs, attached thereto. According to some embodiments, the light intensity and / or wavelength of the LEDs may be changed using one or more algorithms, as described in more detail elsewhere herein.

[0427] Advantageously, illuminating the moving element may enable an optical sensor to detect faults and / or surface and / or structural defects by analyzing shadows and / or reflections. For example, a surface defect may give rise to a shadow that can be analyzed by one or more algorithms to be detected as a surface defect.

[0428] Advantageously, illuminating the movable element to detect surface defects while receiving optical signals from one or more optical sensors may enable detection of defects and / or faults that may be invisible to the human eye. According to some embodiments, the size of the defects and / or faults may be in a range between 10 micrometers and 5 mm. According to some embodiments, the size of the defects and / or faults may be less than 10 micrometers. According to some embodiments, the size of the defects and / or faults may be greater than 5 mm.

[0429] Reference is now made to FIG. 13, which is a simplified block diagram of a system for monitoring the status and / or integrity of operation of a movable element, optionally fixed at at least one end thereof to a stationary element with respect to the element's movement and / or rotation, according to some exemplary embodiments of the present invention. The system 1300 includes at least one optical sensor 1301, a processor 1302, and a memory module 1303 configured to store an algorithm 1304 (fault detection algorithm) for execution by the processor 1302. The at least one optical sensor 1301 is configured to be fixed on, proximate to, and / or in view of the movable element and configured to capture multiple images of the movable element while it is moving. The processor 1302 is configured to communicate with the at least one optical sensor 1301 and is executable to receive the multiple captured images from the at least one optical sensor 1301. The processor 1302 is executable to calculate the maximum amplitude of the secondary movement of the movable element and / or the displacement of the movable element along an axis perpendicular to the axis of movement of the movable element and / or the axial and radial movements of the movable element and / or combinations thereof. If the maximum amplitude and / or displacement of the secondary movement of the moving element exceeds a predefined threshold, the processor 1302 is executable to output a signal indicative of a fault in or associated with the moving element.

[0430] According to some embodiments, one possibility for calculating the maximum amplitude of the secondary movement of the movable element and / or the displacement of the movable element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the movable element and / or a combination thereof is to measure the contour, circumference, and structure of the imprint formed along the movement of the movable element in each of the multiple images and calculate the maximum deviation of the imprint from a known baseline of what the imprint would be if it were operating properly. For example, according to some embodiments, at least one optical sensor 1301 can be configured to use a relatively long exposure time, so that motion blur appears in the multiple images of the movable and / or rotationally movable element as a result of the element's movement. The motion blur appearing in the axis perpendicular to the element's movement and / or along the axial and radial movements of the movable element is indicative of the movement of the movable and / or rotationally movable element due to the condition and / or integrity of the movement of the movable element, and the greater the blur in the axis perpendicular to the axis of the movable element, the stronger the indication of a fault or problem in the condition and / or integrity of the movement of the movable element. The processor 1302 is executable to calculate a maximum deviation of the blur from a known baseline of the blur formed when capturing an image of the movable and / or rotationally movable element when operating properly, and if the deviation exceeds a predefined threshold, the processor 1302 is executable to output a signal indicative of a fault in or associated with the movable element.

[0431] According to some embodiments, another possibility for calculating the maximum amplitude of the secondary movement of the movable element and / or the displacement of the movable element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the movable element and / or a combination thereof is obtained by utilizing an illumination light source configured to illuminate the movable element with light pulses during the capture of multiple images, during which the pulse duration is shorter than the shutter exposure time of at least one sensor 1301. In this case, the processor 1302 is operable to calculate the maximum amplitude of the secondary movement of the movable element and / or the frequency of the secondary movement of the movable element and / or the displacement of the movable element along an axis perpendicular to the axis of motion and / or the axial and radial movements of the movable element and / or a combination thereof. According to some embodiments, monitoring the operation status and / or integrity of the movable and / or rotary movable element comprises monitoring at least one element of tension, tension, integrity, density, straightness, stability, dynamic balance, frequency, symmetry, stiffness and / or alignment of the movable and / or rotary element.

[0432] According to some embodiments, the predefined threshold is based on the maximum amplitude of the secondary movement of the looped moving element and / or the displacement of the looped moving element along an axis perpendicular to the axis of movement and / or the axial and radial movements of the looped moving element and / or combinations thereof, calculated when operating properly.

[0433] According to some embodiments, the movable element may be fixed at its ends.

[0434] Reference is now made to FIG. 14, which is a simplified flow diagram of a method for monitoring the status and / or integrity of operation of a movable element, optionally fixed at at least one end thereof to a stationary element with respect to the movement and / or rotation of the movable element, in accordance with some embodiments of the present invention. In step 1401, at least one optical sensor is configured to capture multiple images of the movable element while it is moving. In step 1402, a processor is configured to receive the multiple images from the at least one optical sensor. In step 1403, the processor is executable to calculate the maximum amplitude of the secondary movement of the movable element and / or the displacement of the movable element along an axis perpendicular to the axis of movement of the movable element and / or a combination thereof. In step 1404, if the maximum amplitude of the secondary movement of the movable element and / or the displacement of the movable element are above a predefined threshold, the processor is executable to output a signal indicating a fault in or associated with the movable element.

[0435] According to some embodiments, the moving element may be a belt connected between two pulleys. The belt may be a toothed belt, for example a timing belt, or a flat belt.

[0436] 15A is a simplified schematic diagram of a first example of monitoring the tension and / or tension of a belt coupled between two pulleys, according to some exemplary embodiments of the present invention. Belt 1501 is coupled between pulleys 1502a and 1502b (referred to herein as pulley 1502) and moves between the two pulleys 1502 in the direction indicated by arrow 1503.

[0437] At least one optical sensor, such as at least one sensor 1301, is configured to capture multiple images of the belt 1501 while it is moving. A processor, such as processor 602, is operable to receive the multiple images and calculate a maximum amplitude reached by the belt 1501 in an axis 1505 perpendicular to the axis 1504 of motion of the belt 1501. The distance is experienced as a result of secondary motion of the belt 1501 in the axis 1505 caused by slack in the belt 1501. According to some embodiments, the amplitude may be calculated by adding the maximum distance reached by the belt 1051 along the positive direction of the axis 1505 to the maximum distance reached by the belt 1051 along the negative direction of the axis 1505. For example, the amplitude may be calculated by measuring the motion blur caused in each of the multiple images due to the belt secondary motion in the axis 1505 and adding the two maximum lengths of blur measured along the positive and negative directions of the axis 1505. Another example for calculating the maximum amplitude of the belt 1501 distance traveled along the axis 1505 can be achieved by capturing multiple images while illuminating the belt 1501 with a light pulse such that the pulse duration is shorter than the shutter exposure time of at least one optical sensor, thereby receiving a clear image of the belt 1501, and calculating the maximum amplitude of the belt 1501 distance traveled by adding the maximum distance the belt traveled along the positive direction of the axis 1505 to the maximum distance the belt traveled along the negative direction of the axis 1505. After the maximum amplitude of the belt 1501 distance traveled is calculated, the processor can be operable to compare the calculated amplitude with a predefined threshold to detect slack in the belt 1501. The predefined threshold is based on a known baseline of belt blur occurring in one or more images when the belt is operating with proper tension / tension. If the calculated maximum amplitude of the blur is above the predefined threshold, the processor can be operable to output a signal indicating detected slack in the belt.

[0438] According to some embodiments, the belt may be a flat belt or a toothed belt, such as a timing belt.

[0439] According to some embodiments, the detected fault may be a loose bell, a misalignment between two pulleys, or an asymmetry in the mechanism of the belt and two pulleys.

[0440] The belt failure mode can be a slipped belt or a critical level of belt slack.

[0441] 15B is a simplified schematic diagram of a second example of monitoring the tension and / or tension of a belt coupled between two pulleys, according to some embodiments. In this case, the displacement of belt 1501 on pulley 1502 is calculated along the axis indicated by arrow 1510.

[0442] If the amplitude of the displacement of the belt 1501 exceeds a predefined threshold, the processor is executable to output a signal indicating a detected fault, for example, slack in the belt 1501, misalignment between the two pulleys, or asymmetry in the mechanism of the belt and the two pulleys.

[0443] According to some embodiments, the moving element may be a shaft (e.g., the shaft of FIGS. 6G-6H). According to some embodiments, at least one sensor, such as at least one sensor 1301, is configured to capture multiple images of the rotating shaft. The at least one sensor is set at a shutter exposure time longer than that required to capture a clear image, or a frames per second (FPS) rate lower than that required to capture a clear image (e.g., a standard 30, 60, or 120 FPS image sensor), thereby causing motion blur in the multiple captured images of the shaft. In this exemplary embodiment, a processor, such as processor 1302, is configured to calculate the amplitude of the blur along the axis of the shaft and the radial movement of the shaft and detect when the amplitude of the blur exceeds a predefined threshold; accordingly, the processor is configured to output a signal indicative of a detected curvature in the shape of the shaft (e.g., at the end or middle of the shaft).

[0444] According to some embodiments, the element may be a rotor, a propeller, a fan blade, an impeller, a turbine blade and / or a turbo blade.

[0445] According to some embodiments, the processor is further executable to apply a set of fault detection algorithms to the plurality of images to detect potential faults in the element or a section thereof based on predefined fault detection parameters, and to output a signal indicative of any detected faults.

[0446] According to some embodiments, the fault detection algorithm is configured to acquire data associated with fault detection parameters for at least one failure mode of the element, identify at least one change in at least one of the plurality of images by comparing the given image of the element in a suitable state or by comparing the image with a previously acquired image of the element, apply the at least one identified change to an algorithm configured to analyze the identified change and classify whether the identified change is associated with the failure mode of the element, thereby labeling the identified change as a detected fault based at least in part on the acquired data, and, for an identified change classified as associated with the failure mode, output a signal indicative of the identified change associated with the failure mode.

[0447] According to some embodiments, for a detected fault, the processor is executable to generate at least one model of a trend in the identified fault. The trend may include a rate of change of the fault. According to some embodiments, generating the at least one model of a trend in the detected fault includes calculating a correlation of the rate of change of the fault with one or more environmental parameters.

[0448] According to some embodiments, the processor is further configured to alert a user of a predicted failure based at least in part on the generated model. According to some embodiments, alerting the user of a predicted failure includes any one or more of a time (or time range) of the predicted failure, age of the element and characteristics of the failure mode, or any combination thereof.

[0449] general The terms "comprise," "comprising," "include," "including," "having," and their cognates mean "including, but not limited to."

[0450] The term "consisting of" means "including and limited to."

[0451] As used herein, singular forms such as "a," "an," and "the" include plural references unless the content clearly dictates otherwise.

[0452] Within this application, various quantifications and / or expressions may include the use of ranges. The range format should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, statements containing ranges should be considered to specifically disclose all possible subranges and individual numerical values ​​within that range. For example, statements of a range such as 1 to 6 should be considered to specifically disclose subranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., as well as individual numbers within the stated range and / or subrange, e.g., 1, 2, 3, 4, 5, and 6. Whenever a numerical range is given in this document, it is meant to include any recited number (fractional or integer) within the stated range.

[0453] It is understood that certain features that are described in the context of separate embodiments (e.g., for clarity) may also be provided in combination in a single embodiment. Various features of the present disclosure that are described in the context of a single embodiment (e.g., for brevity) may also be provided separately or in any suitable subcombination or suitable for use in any other described embodiment. For example, methods, sensors, lighting, and processing circuits described in connection with some embodiments may also be used in other embodiments. Features described in the context of various embodiments should not be considered essential features of those embodiments unless the embodiment is inoperable without those elements.

[0454] While this disclosure has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, this application is intended to embrace all such alternatives, modifications, and variations that fall within the spirit and broad scope of the appended claims.

[0455] All references (e.g., publications, patents, patent applications) mentioned within this application are incorporated herein by reference in their entirety, e.g., as if each individual publication, patent, or patent application was individually indicated to be incorporated herein by reference. Citation or identification of any reference in this application should not be construed as an admission that such reference is available as prior art to the present disclosure. Additionally, any priority document(s) and / or document(s) (e.g., co-filed applications) related to this application are hereby incorporated herein by reference in their entirety.

[0456] Where section headings are used in this document, they should not necessarily be construed as limiting.

Claims

1. 1. A system for monitoring a moving element, comprising: receiving image data of at least one section of the moving element from at least one optical sensor; determining from the image data a range of movement of the at least one segment of the movable element in a secondary direction of movement relative to a primary direction of movement of the movable element; outputting an indicator of the health of the moving element based on the analysis of the range of motion; a processing circuit configured to perform the

2. The system of claim 1 , wherein the movable element comprises a rotationally movable element, the primary direction of movement comprises rotational movement, and the secondary direction of movement comprises linear movement.

3. 2. The system of claim 1, wherein the movable element comprises a looped movable element, the primary direction of movement comprises a first longitudinal movement of the looped movable element, and the secondary direction of movement is transverse to the first longitudinal movement.

4. The system of claim 3 , wherein the transverse movement includes a second longitudinal movement perpendicular to the first longitudinal movement.

5. The loop-shaped movable element comprises: Pulley belt; cable; strap; rope; and chain, The system of claim 3 , comprising one of:

6. The system of claim 1 , wherein the processing circuitry is further configured to control operation of the moving element based on the health of the moving element to prevent operation of the moving element during a fault.

7. The system of claim 1 , wherein the indicator is output to a controller configured to control operation of the moving element based on the health of the moving element to prevent operation of the moving element during a fault.

8. The system of claim 1 , wherein the indicator is output to a preventative maintenance system configured to provide maintenance instructions based on the indicator.

9. The system of claim 1 , wherein the indicator is displayed on a user interface to alert a user to the health of the moving component.

10. The indicators are: Maintenance instructions; time-to-failure estimates; Fault alerts; and Operational instructions in response to detected faults; The system of claim 1 , comprising at least one of:

11. 10. The system of claim 1, wherein the moving element comprises a looped moving element, and wherein, for the analysis of the range of motion, a larger range of motion indicates a lower tension in the moving element compared to a tension at a smaller range of motion.

12. The system of claim 1 , wherein the analysis includes assessing the health of the moving element by comparing a magnitude of the range of motion to at least one threshold.

13. 2. The system of claim 1, wherein the processing circuitry determines the range of motion by calculating the maximum amplitude of the contour of the moving element in an image captured with an exposure time greater than the time expected for the moving element to move through its entire range of motion.

14. The system of claim 13 , wherein the exposure time is selected to blur the moving element in the image data.

15. 14. The system of claim 13, wherein the processing circuitry is further configured to control the optical sensor to capture the image at an exposure time that exceeds the expected time for the movable element to move through the range of motion.

16. The system of claim 1 , wherein the processing circuitry determines the range of motion based on a statistical analysis of a series of image frames.

17. The system of claim 1 , wherein the image data comprises a video sequence of images.

18. The system of claim 1 , wherein the processing circuitry is configured to assess the health of the moving element based on a change in magnitude of the range of motion over time.

19. The system of claim 1 , wherein the processing circuitry is configured to assess the health of the moving element based on a shape of the at least one segment.

20. The system of claim 1 , wherein the processing circuitry is configured to assess the health of the moving element based on changes in shape of the moving element in a plurality of different sections of the moving element.

21. The system of claim 1 , wherein the processing circuitry is configured to receive image data from a plurality of optical sensors, each of the optical sensors capturing image data for a respective segment of the moving element.

22. The system of claim 1 , wherein the processing circuitry is configured to receive image data from a single optical sensor that is in a fixed position relative to the moving element.

23. The system of claim 1 , wherein the at least one section of the moving element comprises at least 10% of the length of the moving element.

24. The system of claim 1 , wherein the indication is obtained from a data structure indexed, at least in part, by at least one parameter determinable from the range of motion.

25. The system of claim 1 , wherein the analysis is based on a machine learning model trained using a training set of images collected during operation of at least one of the moving element and a similar moving element.

26. 26. The system of claim 25, wherein the machine learning model is a neural network.

27. 26. The system of claim 25, wherein the training of the machine learning model is performed using a supervised learning algorithm.

28. 26. The system of claim 25, wherein the training of the machine learning model is performed using an unsupervised learning algorithm.

29. The system of claim 1 , wherein the analysis includes predicting future health of the moving element by performing a trend analysis on changes in the range of motion over time.

30. 1. A method for monitoring a moving element, comprising: receiving image data of at least one section of the moving element from at least one optical sensor; determining from the image data a range of movement of the at least one segment of the movable element in a secondary direction of movement relative to a primary direction of movement of the movable element; outputting an indicator of the health of the moving element based on the analysis of the range of motion; A method comprising:

31. 31. The method of claim 30, wherein the movable element comprises a rotationally movable element, the primary direction of movement comprises rotational movement, and the secondary direction of movement comprises linear movement.

32. 31. The method of claim 30, wherein the movable element comprises a looped movable element, the primary direction of movement comprises a first longitudinal movement of the movable element, and the secondary direction of movement is transverse to the first longitudinal movement.

33. 31. The method of claim 30, further comprising controlling operation of the moving element based on the health of the moving element to prevent operation of the moving element during a fault.

34. 31. The method of claim 30, wherein the indication is output to a controller configured to control operation of the moving element based on the health of the moving element to prevent operation of the moving element during a fault.

35. The method of claim 30 , wherein the indication is output to a preventative maintenance system configured to provide maintenance instructions based on the indication.

36. 31. The method of claim 30, further comprising displaying the indicator on a user interface to alert a user to the health of the moving element.

37. The indicators are: Maintenance instructions; time-to-failure estimates; Fault alerts; and Operational instructions in response to detected faults; 31. The method of claim 30, comprising at least one of:

38. 31. The method of claim 30, wherein the moving element comprises a looped moving element, and wherein, for the analysis of the range of motion, a larger range of motion indicates a lower tension in the moving element compared to a tension at a smaller range of motion.

39. 31. The method of claim 30, wherein the analysis includes assessing the health of the moving element by comparing a magnitude of the range of motion to at least one threshold.

40. 31. The method of claim 30, wherein determining the range of motion includes calculating a maximum amplitude of a profile of the moving element visible in an image captured with an exposure time greater than the time expected for the moving element to move through its entire range of motion.

41. 41. The method of claim 40, further comprising controlling the optical sensor to capture an image of the moving element at an exposure time that exceeds the expected time for the moving element to move through the range of motion.

42. The method of claim 30, wherein the range of motion is determined based on a statistical analysis of a series of image frames.

43. The method of claim 30, wherein the image data comprises a video sequence of images.

44. 31. The method of claim 30, further comprising assessing the health of the moving element based on a change in magnitude of the range of motion over time.

45. The method of claim 30, further comprising assessing the health of the moving element based on a shape of the at least one segment.

46. 31. The method of claim 30, further comprising assessing the health of the moving element based on changes in the shape of the moving element in multiple sections of the moving element.

47. 31. The method of claim 30, wherein the image data includes images provided by a plurality of optical sensors, each of the optical sensors capturing an image of a respective section of the moving element.

48. 31. The method of claim 30, wherein the image data is provided by a single optical sensor in a fixed position relative to the moving element.

49. 31. The method of claim 30, wherein the at least one section of the movable element comprises at least 10% of the length of the movable element.

50. 31. The method of claim 30, wherein the indication is obtained from a data structure indexed, at least in part, by at least one parameter determinable from the range of motion.

51. 31. The method of claim 30, wherein the analysis is based on a machine learning model trained using a training set of images collected during operation of at least one of the moving element and a similar moving element.

52. 52. The method of claim 51, wherein the machine learning model is a neural network.

53. 52. The method of claim 51 , wherein the training of the machine learning model is performed using one of a supervised learning algorithm or an unsupervised learning algorithm.

54. 31. The method of claim 30, wherein the analysis includes performing a trend analysis on changes in the range of motion over time to predict the future health of the moving element.

55. A non-transitory storage medium storing program instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 30 to 54.

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