System and method for health assessment and predictive maintenance of machines with axes of motion

Abstract

A machine having a motion axis is monitored without relying on additional sensors. The first control data is collected and analyzed to determine a feature. Second control data is collected and compared to the signature to identify a difference between the second control data and the signature. If the difference exceeds a predetermined threshold, one or more actions are performed to address the difference between the second control data and the signature.

Description

Technical Field

[0001] This disclosure relates to a method for assessing the condition of a machine having one or more motion axes, a system for performing one or more health assessments, and a computing device for assessing the condition of a machine having one or more motion axes. Background Technology

[0002] Industrial systems require regular maintenance to ensure safe and reliable operation over time. Some known maintenance strategies, such as planned or scheduled maintenance, involve performing actions based on a predetermined schedule rather than the actual condition of the equipment. However, these strategies often result in unnecessary downtime and costs due to premature maintenance or repairs. To mitigate these issues, at least some industrial systems employ fault detection solutions to monitor equipment and schedule maintenance only when a potential fault or anomaly is detected. However, traditional fault detection solutions typically require the installation of additional sensors to monitor the equipment, which can be costly and / or impractical, especially for large and / or complex systems that require numerous sensors. Summary of the Invention

[0003] This disclosure allows for device monitoring without relying on additional sensors. In one aspect, a method is provided for assessing the condition of a machine having one or more motion axes. The method includes collecting first control data, analyzing the first control data to determine a signature, collecting second control data, comparing the second control data with the signature to identify differences between the second control data and the signature, and performing one or more actions to resolve the differences between the second control data and the signature if the differences exceed a predetermined threshold.

[0004] On the other hand, a system for performing one or more health assessments is provided. The system includes a machine having one or more motion axes, and a computing device configured to collect first control data, analyze the first control data to determine a signature, collect second control data, compare the second control data with the signature to identify differences between the second control data and the signature, and, if the difference exceeds a predetermined threshold, perform one or more actions to resolve the differences between the second control data and the signature.

[0005] In another aspect, a computing device is provided for evaluating the condition of a machine having one or more motion axes. The computing device includes a processor and a computer-readable storage device including one or more instructions, the one or more instructions being configured to cause the processor to collect first control data, analyze the first control data to determine a signature, collect second control data, compare the second control data with the signature to identify a difference between the second control data and the signature, and, if the difference exceeds a predetermined threshold, perform one or more actions to resolve the difference between the second control data and the signature.

[0006] Other aspects and features of this disclosure will be apparent in part and pointed out in part herein. This summary is provided to introduce some concepts in a simplified form, which will be further described in the detailed description below. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used alone as an aid in determining the scope of the claimed subject matter. Attached Figure Description

[0007] The various aspects of this disclosure are described in detail below with reference to the accompanying drawings, in which:

[0008] Figure 1 This is a block diagram illustrating an example system including a machine with a motion axis;

[0009] Figures 2 to 9 Includes line graphs showing example motion control data that can be used to control and / or manage machines with motion axes;

[0010] Figure 10 Including providing systems (such as Figure 1 A screenshot of an example system dashboard providing an overview of the system shown.

[0011] Figure 11 Includes screenshots of the example axis interface, which provides information to the system (such as...). Figure 1 An overview of the shafts or machines within the system shown;

[0012] Figure 12 Including systems (such as Figure 1 A screenshot of an example trajectory interface characterized by the trajectory of an axis or machine within the system shown.

[0013] Figure 13 and Figure 14 This includes showing what can be done via a user interface (such as...) Figure 12 The trajectory interface shown presents a line graph of the example trajectory;

[0014] Figure 15 Including systems (such as Figure 1 A screenshot of an example notification interface characterized by a timeline of events within the system shown.

[0015] Figure 16 This is a flowchart illustrating an example method for evaluating the condition of a machine with one or more motion axes; and

[0016] Figure 17 This illustrates one or more computational operations that can be used to perform the computations described herein (such as including...). Figure 16 The computer architecture diagram of the computing system shown in the methods is shown.

[0017] In all the accompanying drawings, the corresponding reference numerals denote the corresponding parts. Detailed Implementation

[0018] According to various examples of this disclosure, equipment monitoring is used to perform health assessments and predict maintenance or replacement of machines with motion axes without relying on additional sensors. Machines with motion axes (such as motors, actuators, linear platforms, and rotary tables) include one or more components that move along or about a linear axis. Such machines typically rely on precise motion control data to perform tasks through controlled motion. Machines with motion axes are frequently found in industries such as manufacturing, robotics, CNC machining, and automation, where precise positioning and motion capabilities are essential for performing tasks and operations. However, potential physical or electrical problems in such machines, such as degradation, misalignment, and / or overload, can lead to equipment damage or failure, potentially resulting in mechanical interruptions, machine downtime, production bottlenecks, production losses, and / or reduced manufacturing quality.

[0019] The examples described herein enable the prediction of potential damage or failure before it occurs, thus facilitating proactive maintenance. To achieve this, the examples described herein can utilize the machine itself, or the drive of the control machine, as a “sensor” to detect and / or measure one or more parameters without relying on additional sensors. For example, an increase in the current or energy used to control the machine can indicate friction-related anomalies, while a high variation in the current or energy used to control the machine can indicate backlash-related anomalies. By leveraging existing data sources and employing advanced analytics, the examples described herein allow for proactive maintenance strategies that minimize downtime and reduce operating costs. Furthermore, eliminating the need for additional sensors reduces overall system cost and complexity by decreasing the number of components requiring installation and maintenance, while also increasing overall system reliability by reducing the number of potential points of failure.

[0020] Various aspects of this disclosure allow for the identification of faults and / or anomalies (e.g., faults and / or anomalies related to friction or backlash) using existing data sources by learning or recording patterns in reference signals or control data, in order to calculate or determine a health index for each axis in a machine with motor shafts. In some examples, reference signals and / or control data may be periodically or continuously sampled over a machine cycle and transmitted to an edge device for processing. For example, the edge device may be used to perform time and frequency analysis of the reference signals and / or control data, and / or calculate or determine a health index for each axis in the time and frequency domains. The health index can be monitored over time to assess the current health or condition of each axis and / or the machine, and / or to obtain analytical insights such as performance metrics, efficiency data, operational trends, and / or fault detection. This information can then be uploaded to the cloud to allow one or more remote users to access it through a web-based interface.

[0021] In some examples, the edge device learns normal, healthy operating behavior across all axes and various loads or conditions. This allows the edge device to identify variations or deviations from expected operating behavior, which can indicate or reveal one or more faults and / or anomalies in near real-time. This early detection supports proactive maintenance strategies that mitigate the risk of unexpected system failures. For example, control data collected during a first time period (e.g., first control data) can be used to establish a baseline, which can be compared with control data collected during a second time period (e.g., second control data) to identify one or more differences or deviations from the baseline, which can indicate one or more faults and / or anomalies. When the deviation meets or exceeds a predetermined threshold, an alarm or notification can be presented to one or more users (such as maintenance or quality teams) to prompt predictive maintenance actions. The system allows for customizable alarm management, including the ability to set different thresholds and action types. In this way, the examples described in this article help detect drift from healthy operating behavior and address common industry challenges associated with wear, damage, imbalance, misalignment, gaps, slack, backlash, friction, shock, inappropriate belt tension, fluid noise, insufficient lubrication, cavitation, power quality issues, or circuit problems in motion-controlled machines.

[0022] Various aspects of this disclosure provide a computing system that performs one or more operations in an environment comprising multiple devices coupled to each other via a network (e.g., a local area network (LAN), a wide area network (WAN), or the Internet). The systems and methods described herein can be implemented using computer programming or engineering techniques including computer software, firmware, hardware, or combinations or subsets thereof. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. While any methods and materials similar or equivalent to those described herein may be used in practice or testing of this disclosure, some preferred methods and materials are described below.

[0023] The systems and methods disclosed herein provide technical solutions to technical problems by leveraging existing motion control data to gain valuable insights into the health of machines and / or the entire system. The technical effects of the systems and methods described herein are achieved by using a computational system configured to perform one or more of the following operations: (i) collecting first control data; (ii) analyzing the first control data to determine a signature; (iii) collecting second control data; (iv) comparing the second control data with the signature to identify differences between the second control data and the signature; (v) analyzing one or more time-domain parameters in the second control data to determine whether the second control data indicates one or more friction-related anomalies; (vi) analyzing one or more frequency-domain parameters in the second control data to determine whether the second control data indicates one or more backlash-related anomalies; and / or (vii) performing one or more actions to resolve the differences between the second control data and the signature.

[0024] Figure 1 An example system 100 including a machine with a motion axis is shown. In some examples, system 100 is configured to drive load 110 via gearbox 120 using motor 130 controlled by driver 140 powered by power supply 150.

[0025] Load 110 is a mechanical device or apparatus configured to perform one or more tasks or operations. Load 110 and its tasks can vary depending on the application. Example loads 110 may include, but are not limited to, conveyor systems, pumps, compressors, mixers, agitators, fans, blowers, machine tools, material handling equipment, packaging machines, presses, and robotic systems. In some examples, load 110 itself may be a machine with a motion axis configured to perform its tasks through controlled movement. For example, load 110 may be or include a CNC machine tool and / or an automated packaging system, where the CNC machine tool uses its motion axis to precisely cut and shape materials, and the automated packaging system uses its motion axis to accurately and consistently place products into boxes.

[0026] Gearbox 120 is a mechanical device or apparatus configured to provide or deliver mechanical power to load 110, thereby enabling it to perform its tasks. For example, gearbox 120 may provide rotational energy to the spindle of a CNC machine tool to control a cutting tool with high precision and / or to the arms and conveyors of an automated packaging system to ensure accurate and efficient product handling. In some examples, gearbox 120 is used to regulate and / or manage the amount of rotational speed and / or torque transmitted to load 110 to meet operational requirements. For example, gearbox 120 may include multiple gears that mesh together to transmit rotational power based on their gear ratios, enabling it to regulate the rotational speed and / or the amount of torque applied to drive load 110.

[0027] Motor 130 is an electromechanical device or apparatus configured to convert electrical energy from driver 140 and / or power source 150 into mechanical power, which is supplied or delivered to gearbox 120 to drive load 110. For example, motor 130 may include a rotor that rotates or swirls about a rotation axis 152 to generate mechanical power when energized by power source 150. In some examples, driver 140 may be used to control the operation and / or performance of motor 130.

[0028] The driver 140 is an electrical device or apparatus configured to provide or supply electrical signals to the motor 130 to regulate and / or manage the operation and / or performance of the motor 130. For example, the driver 140 can adjust control parameters, such as the frequency, voltage, and / or current of the electrical input to the motor 130, to regulate and / or manage one or more operating parameters, such as the position, speed, acceleration, force, and / or torque of the motor 130. In some examples, these adjustments are based on one or more physical properties and / or environmental conditions, such as those detected or determined by one or more feedback mechanisms or predetermined parameters. In this way, the driver 140 allows for precise motion control capabilities, thereby enabling dynamic adjustments that enhance the performance and / or efficiency of the motor 130 and the overall system 100.

[0029] like Figure 1As shown, system 100 may include one or more edge devices 160 configured to perform one or more computational tasks, such as data aggregation, filtering, analysis, and / or running machine learning models. These computational tasks support monitoring the operation and / or performance of system 100 and its components, including load 110, gearbox 120, motor 130, driver 140, and power supply 150. In some examples, edge device 160 may be configured to utilize components of system 100 (e.g., load 110, gearbox 120, motor 130, driver 140, power supply 150, etc.) "as sensors" to detect and / or measure one or more operational and / or load conditions without relying on additional sensors. For example, edge device 160 can communicate with load 110, gearbox 120, motor 130, driver 140, and / or power supply 150 to collect and / or analyze various types of existing data, including but not limited to operating status, start-up time, stop time, running time, power input, power output, power consumption, power loss, power quality, position, speed, acceleration, force, torque, frequency, voltage, current, operating temperature, and / or diagnostic codes. In this way, the state of components downstream of the motor (e.g., kinematic changes) can be deduced.

[0030] In some examples, edge device 160 acquires and / or receives motion control data 162 associated with one or more machines having motion axes (e.g., load 110 and / or motor 130), and analyzes the motion control data 162 to assess health or performance, detect anomalies, and / or predict maintenance needs. For example, motion control data 162 can be used to identify or determine the efficiency, performance, and / or condition of system 100 and / or one or more of its components (e.g., load 110, gearbox 120, motor 130, drive 140, power supply 150, etc.). Example motion control data 162 may include, but is not limited to, position, speed, acceleration, force, torque, frequency, voltage, and current. This data allows for the derivation of the health status of the kinematic chain associated with motor 130.

[0031] To reduce latency and / or ensure real-time or near real-time processing, edge device 160 can be located close to the monitored components of system 100. In some examples, edge device 160 can be communicatively coupled to one or more remote computing devices (not shown) via network 170 to facilitate remote monitoring and / or management of system 100. In this way, network 170 can allow one or more adjustments, diagnostics, and / or maintenance to be performed using the remote computing devices.

[0032] Figure 2A set of example motion control data 162 for controlling and / or managing machines with motion axes (such as load 110 and / or motor 130) is shown. These three sets illustrate how various problems can be detected or identified by analyzing changes in motion control data 162 over time. For example, the first set of motion control data 162 collected during a first time period shows a first current pattern 210 corresponding to normal engine torque under typical operating conditions. This first current pattern 210 can be used to establish or define a standard or signature 212 for baseline operating behavior.

[0033] The second set of motion control data 162 collected during the time period following the first time period shows a second current pattern 220, which can be compared with the signature 212 to determine if there are any changes or deviations from the baseline operating behavior. Figure 2 As shown, the second current pattern 220 extends substantially beyond signature 212, thereby indicating a higher current draw compared to the first current pattern 210. This increased current draw associated with the second current pattern 220 illustrates the effect of increased friction within system 100, as more current is required to overcome frictional forces.

[0034] A third set of motion control data 162, collected during a period following the first time period, illustrates a third current pattern 230. This third current pattern 230 can be compared to the signature 212 to determine if any changes or deviations exist from the baseline operating behavior. Figure 2 As shown, the third current pattern 230 is generally more unstable than the signature 212, thus indicating an irregular current draw compared to the first current pattern 210. This irregular current draw associated with the third current pattern 230 illustrates the effect of backlash or mechanical slack within system 100, as additional adjustments are required to address mechanical clearance or misalignment.

[0035] Motion control data 162 associated with a first time period (e.g., a first current mode 210) may be referred to as first control data, and motion control data 162 associated with time periods following the first time period (e.g., a second current mode 220, a third current mode 230) may be referred to as second control data. In some examples, the difference or deviation between the first control data and the second control data may be associated with one or more identified factors, including but not limited to changes in task, operating state, system configuration, load conditions, and / or environmental conditions. Additionally or alternatively, at least some differences or deviations between the first control data and the second control data may be associated with one or more previously unidentified factors, including but not limited to wear, damage, imbalance, misalignment, clearance, slack, backlash, friction, shock, inappropriate belt tension, fluid noise, insufficient lubrication, cavitation, power quality problems, or circuit problems.

[0036] Figure 3-5 Other sets of example motion control data 162 for controlling and / or managing the position of machines with motion axes (such as load 110 and / or motor 130) are shown. Figure 3-5 As shown, motion control data 162 can be represented by two line graphs: a first line graph 310 and a second line graph 320. The first line graph 310 performs time analysis by plotting the change of one aspect of motion control data 162 (such as current) over time, and the second line graph 320 performs spectral analysis by plotting another aspect of motion control data 162 (such as amplitude) relative to yet another aspect of motion control data 162 (such as frequency). Alternatively, motion control data 162 can be represented in any other format or manner that enables the methods and systems described herein to function and / or operate as described herein.

[0037] Figure 3 A first set of exemplary motion control data 162 (e.g., first control data) from a first time period is shown. A first line graph 310 illustrates how the periodic current roughly follows a sinusoidal pattern over time, and how it produces corresponding locations that mirror the same sinusoidal pattern. Analysis is possible. Figure 3 The first line graph 310 shown is used to establish or define a standard or signature 212 of baseline operational behavior in the time domain. For example, the first line graph 310 can be analyzed to identify statistical measurements such as mean, variance, standard deviation, peak, trough, peak-to-peak (or trough-to-trough) values, and root mean square (RMS) values. Monitoring motion control data 162 in the time domain can reveal or indicate instantaneous events and / or time-varying characteristics such as mechanical or physical changes.

[0038] The second line plot 320 represents motion control data 162 in the frequency domain, showing how the amplitude changes with frequency. The second line plot 320 reveals a trend of monotonically increasing amplitude with frequency, indicating that higher frequencies are associated with larger amplitudes. The second line plot 320 also presents the multimodal distribution of amplitude along the frequency axis, marked by different peaks or spikes at specific frequencies where significant responses or energy levels are observed. Figure 3 The second line plot 320 shown can be used to establish a standard or signature 212 of baseline operating behavior in the frequency domain. For example, the second line plot 320 can be analyzed to identify inherent resonant frequencies, harmonics, and / or other spectral characteristics. Monitoring motion control data 162 in the frequency domain can reveal frequency-related phenomena such as resonance, harmonic content, mechanical backlash, gear meshing irregularities, and / or frequency-specific responses.

[0039] Figure 4 A second set of exemplary motion control data 162 (e.g., second control data) from a time period following the first time period is shown. Figure 4As shown, the second control data can be compared with data from... Figure 3 The first control data is overlapped or compared to identify or determine one or more differences or increments that may indicate one or more faults and / or anomalies 330. For example, Figure 4 The first increment 332 in the current in the time domain is shown, while the amplitude in the frequency domain remains unchanged or relatively similar. Figure 4 The increment 330 shown (e.g., the first increment 332) can indicate a friction-related anomaly affecting current control, which is typically manifested as increased resistance or heat due to friction affecting system 100.

[0040] Figure 5 A third set of exemplary motion control data 162 (e.g., second control data) from a time period following the first time period is shown. (Compared to...) Figure 4 Same, Figure 5 It also shows the relationship with the source Figure 3 The first control data overlaps with or is compared with the second control data. Figure 5 The diagram shows a second increment 334 in the current in the time domain, a third increment 336 in the amplitude in the frequency domain, and a fourth increment 338 in the case of new peaks or spikes in the amplitude at different frequencies. Figure 5 The increments 330 shown (e.g., second increments 334, third increments 336, fourth increments 338) can indicate backlash-related anomalies affecting current control, which are typically caused by mechanical backlash or misalignment that leads to irregularities in gear engagement and / or position control.

[0041] Figures 6 to 9 A collection of example motion control data 162 for controlling and / or managing machines with motion axes, such as load 110 and / or motor 130, is shown. Figure 6 Motion control data 162 (e.g., first control data) collected during the first time period is shown. Figure 6 The first control data shown includes a line graph illustrating how the amplitude changes over time. Alternatively, the first control data can be represented in any other format or manner that enables the methods and systems described herein to function and / or operate as described herein. Figure 6 As shown, the first control data can be plotted as multiple overlapping traces 610 to facilitate the identification or determination of one or more patterns. For example, Figure 6 Each trace 610 shown represents a single cycle of amplitude, and the overlap of traces 610 provides a comprehensive view of amplitude variation within a first time period. Traces 610 can be used to establish or define one or more ranges or zones of operational behavior. For example, Figure 6 The range 612 of the amplitude covered by the trace 610 is shown, indicated on the scale located next to the line graph.

[0042] Figure 7 It shows that it can be obtained from Figure 6 The example classification system 620 is derived from or generated from the motion control data 162 and / or trace 610 shown. Classification system 620 facilitates quantitative analysis of deviations from expected operational behavior, thereby allowing for early detection of potential problems and supporting proactive maintenance strategies to mitigate the risk of unexpected system failures. Figure 7 As shown, classification system 620 can depict or represent the criteria or signature 212 of baseline operational behavior as a first drawn line 622. Additionally or alternatively, classification system 620 can depict or represent one or more other drawn lines that depict or define one or more boundaries and / or areas of operational behavior. For example, Figure 7 The diagram shows a health zone 624 defined between the upper drawn line 626 and the lower drawn line 628, which is consistent with... Figure 6 The range 612 shown is aligned or corresponds to. Alternatively, the health zone 624 can be defined as covering any range that enables the methods and systems described herein to function and / or operate as described herein.

[0043] In some examples, the classification system 620 may include one or more warning zones 634 to provide a buffer against deviations from the healthy zone 624. For example, Figure 7 The upper warning area 634, located above the health area 624, and the lower warning area 634, located below the health area 624, are shown. (See diagram.) Figure 7 As shown, the upper warning area 634 may be defined between the upper drawn line 636 and the upper drawn line 626 of the health area 624, and the lower warning area 634 may be defined between the lower drawn line 638 and the lower drawn line 628 of the health area 624. Alternatively, the warning area 634 may be defined to cover any range that enables the methods and systems described herein to function and / or operate as described herein.

[0044] In some examples, classification system 620 may include one or more alarm zones 644 to signal or identify significant deviations from the health zone 624, which could be symptoms of one or more malfunctions and / or abnormalities in system 100. For example, Figure 7 The upper alarm zone 644, located above the health zone 624, and the lower alarm zone 644, located below the health zone 624, are shown. Figure 7 As shown, the upper alarm zone 644 can be defined as extending beyond (i.e., above) the upper drawn line 636 of the upper alarm zone 634, and the lower alarm zone 644 can be defined as extending beyond (i.e., below) the lower drawn line 638 of the lower alarm zone 634. Alternatively, the alarm zone 644 can be defined to cover any range that enables the methods and systems described herein to function and / or operate as described herein.

[0045] Figure 8 Example motion control data 162 (e.g., second control data) collected during a time period following the first time period is shown. Figure 8 As shown, the second control data can be plotted as a trace 650, which is related to the data from... Figure 7 The classification system 620 overlaps or compares data to determine whether the second control data reveals or indicates one or more faults and / or anomalies. For example, Figure 8 Show or highlight a segment or portion 652 of the trace 650 extending into the warning area 634, indicating a deviation from the expected operational behavior.

[0046] Figure 9 Other exemplary motion control data 162 (e.g., second control data) collected during a time period following the first time period are shown. Figure 8 similar, Figure 9 The second control data is also plotted as trace 660, which is related to the data from... Figure 7 The classification systems 620 overlap or are compared. Figure 9 Show or highlight a segment or portion 662 of the trace 660 extending into the alarm zone 644, indicating a significant deviation from the expected operational behavior.

[0047] Figure 10 An example system dashboard 700 providing an overview of system 100 is shown. System dashboard 700 includes one or more graphs or pictures 710 of system 100, a tree 720 allowing users to drill down within system 100 via machines and / or axes, and one or more tables, charts, and / or graphs 730 displaying health-related metrics (e.g., motion control data 162) associated with system 100 and / or one or more of its components. Graph 730 enables users to monitor the health of system 100 and make informed decisions based on the data.

[0048] Figure 11An example axis interface 800 is shown, providing an overview of a specific axis (e.g., rotary axis 152) within system 100. The axis interface 800 includes one or more diagrams or pictures 810 of the axis, a window 820 with associated technical specifications and / or operating parameters, a recipe list 822 with predefined operating settings or configurations associated with the axis, and one or more tables, charts, and / or graphs 830 showing health-related metrics associated with the axis (e.g., motion control data 162). The recipe list 822 enables one or more sets of predefined operating settings or configurations (e.g., desired motor speed, acceleration or deceleration rate, torque limits, alarm settings). The graph 830 allows the user to monitor the health of the axis and make informed decisions based on the data. In some examples, the axis interface 800 provides the ability to drill down into detailed information about the axis, including selecting specific parameters and / or time periods for the data displayed on the graph 830.

[0049] Figure 12 An example trace interface 900 is shown, characterized by specific traces (e.g., traces 610, 650, 660) of an axis (e.g., rotation axis 152). The trace interface 900 includes a recipe list 922 with operating recipes or configurations associated with the axis and one or more tables, charts, and / or graphs 930 showing health-related metrics (e.g., motion control data 162) associated with the axis. The graph 930 allows the user to monitor the health of the axis and make informed decisions based on the data. In some examples, the trace interface 900 provides the ability to drill down to browse detailed information about the axis, including selecting specific parameters and / or time periods for the data displayed on the graph 930. Additionally, the trace interface 900 may provide the ability to select another axis for comparison. For example, Figure 13 Two overlapping line graphs 932 are shown, which allow the user to directly compare two separate traces across different axes and / or states. For another example, Figure 14 Two separate line graphs 932 are shown, which allow the user to understand and analyze each trace without interference from the other trace.

[0050] Figure 15 An example notification interface 1000 is shown, characterized by a timeline 1010 including multiple events 1012 within system 100. The timeline 1010 may include one or more markers representing one or more events 1012 (such as notifications and / or alarms) in chronological order. Alternatively, events 1012 may be represented in any other format or manner that enables the methods and systems described herein to function and / or operate as described herein.

[0051] In some examples, the notification interface 1000 provides the ability to change or configure one or more rules and / or parameters for customizing one or more preferences, triggers, and / or actions, thereby allowing customized notification management based on expected needs and / or operational requirements. For example, the notification interface 1000 can be used to define or modify one or more thresholds (e.g., upper drawn line 626, lower drawn line 628, upper drawn line 636, lower drawn line 638) and / or one or more actions triggered when a threshold is met or exceeded. Example actions may include, but are not limited to, generating notifications, alarms, warnings, suggestions, recommendations, prompts, acknowledgments, status bars, and / or control data, and / or using control data to control or manage machines with motion axes. In some examples, the notification interface 1000 can be used to define or modify which aspects, parameters, axes, and / or machines are monitored. Additionally or alternatively, the notification interface 1000 can be used to define or modify timing, priority, urgency, and / or escalation procedures (e.g., if event 1012 is not acknowledged or resolved within a predetermined time frame).

[0052] Figure 16 A method 1100 for assessing the health of a machine (e.g., load 110 and / or motor 130) having one or more motion axes is illustrated. Method 1100 can be performed on all or part of the motion axes. Method 1100 includes collecting first control data associated with one or more axes of the machine at operation 1110. For example, an edge device 160 may communicate with a controller to collect frequency, voltage, and / or current data (e.g., motion control data 162) for adjusting or controlling the rotational axis 152 (e.g., the first axis) of motor 130 during an initial learning period (e.g., a first time period). The controller acts to control the machine. This is where one or more automation programs, I / O management, motion control, etc., can be found.

[0053] At operation 1120, the first control data is analyzed to determine a signature 212 for each axis of the machine. For example, edge device 160 may analyze one or more time-domain parameters in the first control data to establish or define a signature 212 of baseline operating behavior in the time domain. Additionally or alternatively, edge device 160 may analyze one or more frequency-domain parameters in the first control data to establish or define a signature 212 of baseline operating behavior in the frequency domain.

[0054] At operation 1130, second control data associated with each axis of the machine is collected. For example, edge device 160 may communicate with a controller to collect frequency, voltage, and / or current data (e.g., motion control data 162) of the rotating axis 152 (e.g., the first axis) of motor 130 for adjusting or controlling it during subsequent operation periods (e.g., a second time period).

[0055] At operation 1140, the second control data is compared with the corresponding signature 212 to identify differences between the second control data and signature 212 (e.g., increment 330, first increment 332, second increment 334, third increment 336, fourth increment 338). In some examples, edge device 160 may determine whether the differences meet or exceed one or more predetermined thresholds (e.g., upper drawn line 626, lower drawn line 628, upper drawn line 636, lower drawn line 638). For example, it may be determined whether differences in parameters (such as operating state, start time, stop time, running time, power input, power output, power consumption, power loss, power quality, position, speed, acceleration, force, torque, frequency, voltage, current, operating temperature, and / or diagnostic codes) meet or exceed their respective thresholds in the time domain to identify potential friction-related anomalies and / or meet or exceed their respective thresholds in the frequency domain to identify potential backlash-related anomalies. Alternatively, the differences can be compared to their corresponding thresholds in any other domain to identify any other anomalies that enable the methods and systems described herein to function and / or operate as described herein.

[0056] If the difference exceeds a predetermined threshold, one or more actions are performed at operation 1150 to resolve the discrepancy between the second control data and the corresponding signature 212. For example, edge device 160 may automatically generate an alert prompting the user to take action. The alert may include detailed information about the detected problem and recommend or suggest one or more corrective actions. Additionally or alternatively, edge device 160 may generate control data configured to adjust one or more system parameters to mitigate the impact of the detected problem. For example, edge device 160 may use a confirmation dialog to suggest system parameter adjustments to the user and await user approval before implementing the changes. Alternatively, edge device 160 may automatically use the control data to adjust system parameters without user intervention. In some examples, edge device 160 may present an automated action with a status bar notification to the user, allowing the user to monitor the progress of the automated action and / or intervene, override, or adjust the automated action, if needed. In some examples, edge device 160 may act based on the importance, priority, and / or severity of the detected problem. For example, if edge device 160 determines that the detected problem is associated with lower importance, priority, or severity, edge device 160 may decide to automatically present an information notification to the user. As another example, if edge device 160 determines that the detected problem is associated with medium importance, priority, or severity, edge device 160 may decide to automatically present the user with a warning and / or confirmation dialog with recommended actions. As yet another example, if edge device 160 determines that the detected problem is associated with higher importance, priority, or severity, edge device 160 may decide to automatically perform one or more actions without user intervention.

[0057] Figure 17 An example computing system 1200 (e.g., controller, edge device 160) configured to perform one or more computing operations described herein is shown. In some examples, the computing system 1200 includes a processor 1210, a system memory 1220, and a bus 1230 that couples various system components, including the system memory 1220, to the processor 1210.

[0058] Processor 1210 is configured to perform general-purpose computing functions and process data and instructions to perform one or more operations and / or provide other functions described herein. For example, processor 1210 may access system memory 1220 to read data and instructions from system memory 1220 and / or write data and instructions to system memory 1220 for execution of one or more computer-executable instructions. In this way, processor 1210 can be programmed to perform any aspect of the software components described herein, including for performing, implementing, and / or employing motor 130 (… Figure 1 As shown), System Dashboard 700 ( Figure 10 (as shown), axis interface ( Figure 11 As shown), trace interface 900 ( Figure 12 (as shown), Notification interface 1000 ( Figure 15 (as shown), Method 1100 ( Figure 16 The software component shown in the figure. In some examples, processor 1210 may be or include any number of processing units, including central processing units, graphics processing units, field-programmable gate arrays (FPGAs), digital signal processors (DSPs), or other hardware logic components, including but not limited to application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SoCs), complex programmable logic devices (CPLDs), etc.

[0059] System memory 1220 includes any combination of computer-readable media accessible by processor 1210. In some examples, system memory 1220 includes read-only memory (ROM) 1222 storing instructions for performing basic functions and random access memory (RAM) 1224 temporarily storing data and instructions for active use by a program. For example, RAM 1224 may be used to host or store user data, device data, system data, etc., and for performing, implementing, and / or employing motor 130 (… Figure 1 As shown), System Dashboard 700 ( Figure 10 (as shown), shaft interface ( Figure 11 As shown), trace interface 900 ( Figure 12 As shown), notification interface 1000 ( Figure 15 (as shown), Method 1100 ( Figure 16 One or more software components (as shown in the figure).

[0060] Computer-readable media include both communication media and computer storage media. Communication media typically embody computer-readable instructions, data structures, program modules, or other data in modulated data signals, such as carrier waves or other transmission mechanisms. The term "modulated data signal" refers to a signal whose characteristics are set or altered in a way that encodes information in the signal. By way of example and not limitation, communication media include wired media (such as wired networks or direct wired connections) and wireless media (such as acoustic, radio frequency, and infrared media).

[0061] Conversely, computer storage media include tangible media capable of storing information such as computer-readable instructions, data structures, program modules, or other data. By way of example and not limitation, computer storage media include ROM 1222, RAM 1224, hard disk drives (HDDs), solid-state drives (SSDs), external hard disk drives, flash drives, optical storage media (e.g., optical discs (CDs), digital versatile optical discs (DVDs)), and magnetic storage media (e.g., magnetic tape drives). For the purposes of this disclosure, computer storage media are mutually exclusive with communication media and do not include waves, signals, and other temporary or intangible media.

[0062] It should be understood that the software components described herein, when loaded into and executed, can transform the processor 1210 and the entire computing system 1200 from a general-purpose computing system into a dedicated computing system tailored to facilitate the functions described herein. More specifically, computer-executable instructions contained within the software components described herein transform the processor 1210 to operate or function as a finite state machine by specifying how the processor 1210 transitions between states, thereby transforming the transistors or other discrete circuit elements constituting the processor 1210.

[0063] Encoding the software components described herein can also transform the physical structure of the computer-readable medium described herein. In different embodiments of this disclosure, the specific transformation of the physical structure can depend on various factors. Examples of such factors may include, but are not limited to, the technology used to implement the computer-readable medium, whether the computer-readable medium is characterized as a primary storage device or a secondary storage device, etc. For example, if the computer-readable medium is implemented as a semiconductor-based memory, the software disclosed herein can be encoded on the computer-readable medium by transforming the physical states of transistors, capacitors, or other discrete circuit elements constituting the semiconductor-based memory. The software can also transform the physical states of such components to store data thereon.

[0064] As another example, the computer-readable medium disclosed herein can be implemented using magnetic or optical techniques. In such an implementation, when software is encoded therein, the software presented herein can transform the physical state of the magnetic or optical medium. These transformations may include altering the magnetic properties of a specific location within a given magnetic medium. These transformations may also include changing the physical characteristics or properties of a specific location within a given optical medium to alter the optical properties of those locations. Other transformations of the physical medium are possible without departing from the scope and spirit of this specification, wherein the foregoing examples are provided only to facilitate the discussion.

[0065] In some examples, computing system 1200 includes a mass storage device 1240 coupled to processor 1210 for hosting or storing data and instructions, such as operating system 1242, one or more programs 1244, and / or data 1246. Those skilled in the art will understand that copies of at least some of the data and / or instructions hosted or stored in mass storage device 1240 may be temporarily stored in system memory 1220 to enable computing system 1200 to function as described herein.

[0066] like Figure 17 As shown, computing system 1200 can be connected to network 1250 (e.g., network 170) via network interface unit 1252 connected to bus 1230. In this way, computing system 1200 can operate in a networked environment, where computing system 1200 can use one or more remote devices (not shown) to host or store at least some data and / or execute at least some instructions. Computer communication between computing systems can be network transfer, file transfer, applet transfer, email, Hypertext Transfer Protocol (HTTP) transfer, etc.

[0067] In some examples, computing system 1200 may include one or more input / output (I / O) controllers 1260 that facilitate communication and data transfer between processor 1210 and one or more I / O devices (not shown) configured to provide input and / or output capabilities. For example, a user may use one or more input devices, such as a keyboard, pointing devices (e.g., mouse, trackball, touchpad, stylus), microphone, camera, scanner, accelerometer, etc., to input commands and information into computing system 1200. Additionally or alternatively, computing system 1200 may use one or more output devices, such as a monitor, projector, printer, speaker, actuator, etc., to present information in various forms, such as text, images, audio, video, alarms, etc. In some examples, output devices may be integrated with input devices (e.g., in a touchscreen panel or in a controller that includes a vibration component).

[0068] Although this paper refers to computing system 1200 (including edge device 160) Figure 1 (as shown) or included in the edge device 160 ( Figure 1 Some examples are shown and described in the illustrations, but aspects of this disclosure are applicable to any computing system capable of executing computer-executable instructions to implement the operations and functions associated with computing system 1200. It is also conceivable that computing system 1200 may not include... Figure 17 All components shown may include Figure 17 Other components not explicitly shown in the document, or those that can be utilized with Figure 17The architecture shown is completely different from the one described. The computing system 1200 should not be interpreted as having the same architecture as... Figure 17 Any dependencies or requirements relating to any one or combination of the components shown. Computing system 1200 is merely an example of a computing and networking environment for performing one or more computing operations and is not intended to impose any limitation on the scope or functionality of this disclosure.

[0069] This document describes example methods and systems for health assessment and / or prediction of maintenance or replacement of machines with moving axes without relying on additional sensors. For example, changes in one or more control parameters can help diagnose or identify changes in one or more physical characteristics and / or environmental conditions, which can indicate or reveal problems such as increased friction, misalignment, backlash, mechanical degradation, or load variations. The examples described herein monitor control data over time and use this control data for health assessment and / or predictive maintenance. Therefore, the examples described herein provide accurate, user-friendly solutions for effectively detecting and predicting mechanical failures in machines with motor axes (e.g., machines with servo drives). In light of the foregoing, it can be seen that several advantages of the aspects of this disclosure are realized and other advantageous results are obtained.

[0070] Although described in conjunction with example computing system environments, the examples of this disclosure can be implemented using many other general-purpose or special-purpose computing system environments, configurations, or devices. Examples of well-known computing systems, environments, and / or configurations applicable to various aspects of this disclosure include, but are not limited to, server computers, desktop computers, laptop computers, tablet computers, mobile devices, communication devices in the form of wearables or accessories, microprocessor-based systems, multiprocessor systems, programmable consumer electronics, kiosks, desktop devices, industrial control devices, minicomputers, mainframe computers, network computers, distributed computing environments including any of the foregoing systems or devices, etc.

[0071] Examples of this disclosure can be described in the general context of computer-executable instructions (such as program modules) that are executed by one or more computers or other devices as software, firmware, hardware, or a combination thereof. Computer-executable instructions can be organized into one or more computer-executable modules or components. Typically, program modules include, but are not limited to, routines, objects, components, and data structures that perform a particular task or implement a particular abstract data type. Aspects of this disclosure can be implemented with any number and organization of such modules or components. For example, aspects of this disclosure are not limited to the specific computer-executable instructions or specific components or modules shown in the accompanying drawings and described herein. Other examples of this disclosure may include different computer-executable instructions or components having more or fewer functions than those shown and described herein.

[0072] In some instances, the operations illustrated in the figures may be implemented as software instructions encoded on a computer-readable medium, programmed or designed to perform the operations in hardware, or both. For example, aspects of this disclosure may be implemented as a system-on-a-chip or other circuitry comprising multiple interconnected conductive elements.

[0073] One or more elements of an implementation of the device described herein may be used to perform a task or to perform other sets of instructions not directly related to the operation of the device, such as a task related to another operation of a means or system in which the device is embedded. One or more elements of such an implementation may also have a common structure (e.g., a processor for executing code portions corresponding to different elements at different times, a set of instructions executed to perform tasks corresponding to different elements at different times, or an arrangement of electronic and / or optical devices that perform operations for different elements at different times).

[0074] Unless otherwise stated, the execution or order of operations in the examples of this disclosure shown and described herein is not essential. That is, operations may be performed in any order unless otherwise stated, and the examples of this disclosure may include more or fewer operations than those disclosed herein. For example, specific operations are expected to be performed before, simultaneously with, or after another operation within the scope of various aspects of this disclosure.

[0075] The examples shown and described herein, as well as those not specifically described herein but within the scope of various aspects of this disclosure, constitute example apparatus for performing health assessments and / or predicting maintenance or replacement of machines with motion axes. For example, when programmed, coded, or configured to perform… Figure 16 During the operation shown, Figure 1 and Figure 17 The components shown at least constitute an example apparatus for collecting first control data associated with a first axis of one or more motion axes, analyzing the first control data to determine the characteristics of the first axis, collecting second control data associated with the first axis, comparing the second control data with the characteristics to identify differences between the second control data and the characteristics, determining whether the differences exceed a predetermined threshold, and / or performing one or more actions to resolve the differences between the second control data and a signature (e.g., edge device 160).

[0076] When describing elements or examples of aspects of this disclosure, the articles “a,” “an,” “the,” and “described” are intended to indicate the presence of one or more elements. Furthermore, references to “embodiments” or “examples” in this disclosure are not intended to be construed as excluding the existence of additional embodiments or examples that also include the described features. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that additional elements may be present in addition to those listed. The phrase “one or more of the following: A, B, and C” means “at least one of A and / or at least one of B and / or at least one of C.”

[0077] The term "determine" encompasses a wide variety of actions, and therefore, "determine" can include calculation, operation, processing, derivation, investigation, lookup (e.g., searching in a table, database, or other data structure), ascertainment, etc. Furthermore, "determine" can include receiving (e.g., receiving information), accessing (e.g., accessing data in memory), etc. Additionally, "determine" can include parsing, selecting, picking, building, etc.

[0078] In this specification, reference numerals are sometimes used in conjunction with various terms. When a term is used in conjunction with reference numerals, it may mean referring to a specific element shown in one or more drawings. When a term is used without reference numerals, it may mean referring to the term generally and not limited to any particular drawing.

[0079] Having described in detail various aspects of this disclosure, it will be clear that modifications and variations are possible without departing from the scope of the aspects of this disclosure as defined in the appended claims. Since various changes can be made to the above-described constructions, products, and methods without departing from the scope of the aspects of this disclosure, all that is contained in the foregoing description and shown in the accompanying drawings is intended to be illustrative rather than restrictive.

[0080] Although aspects of this disclosure have been described with reference to various examples having their associated operations, those skilled in the art will understand that combinations of operations from any number of different examples are also within the scope of aspects of this disclosure.

Claims

1. A method for evaluating the condition of a machine having one or more motion axes, the method comprising: Collect initial control data; Analyze the first control data to determine the signature; Collect second control data; The second control data is compared with the signature to identify differences between the second control data and the signature; and If the difference exceeds a predetermined threshold, one or more actions are performed to resolve the difference between the second control data and the signature.

2. The method according to claim 1, wherein, The first control data is collected during the initial learning period, and the second control data is collected during the subsequent operation period.

3. The method according to claim 1, wherein, The first control data and the second control data each include frequency, voltage and current.

4. The method according to claim 1, wherein, Analyzing the first control data includes analyzing one or more time-domain parameters in the first control data.

5. The method according to claim 1, wherein, Analyzing the first control data includes analyzing one or more frequency domain parameters in the first control data.

6. The method according to claim 1, wherein, Comparing the second control data with the signature includes analyzing one or more time-domain parameters in the second control data to determine whether the second control data indicates one or more friction-related anomalies.

7. The method according to claim 1, wherein, Comparing the second control data with the signature includes analyzing one or more frequency domain parameters in the second control data to determine whether the second control data indicates one or more backspace-related anomalies.

8. The method according to claim 1, wherein, Performing one or more actions includes performing the one or more actions automatically.

9. A system for performing one or more health assessments, the system comprising: A machine having one or more axes of motion; and A computing device configured to collect first control data, analyze the first control data to determine a signature, collect second control data, compare the second control data with the signature to identify differences between the second control data and the signature, and, if the differences exceed a predetermined threshold, perform one or more actions to resolve the differences between the second control data and the signature.

10. The system according to claim 9, wherein, The computing device collects the first control data during an initial learning period and collects the second control data during a subsequent operation period.

11. The system according to claim 9, wherein, The first control data and the second control data each include frequency, voltage and current.

12. The system according to claim 9, wherein, The computing device analyzes one or more time-domain parameters in the first control data.

13. The system according to claim 9, wherein, The computing device analyzes one or more frequency domain parameters in the first control data.

14. The system according to claim 9, wherein, The computing device analyzes one or more time-domain parameters in the second control data to determine whether the second control data indicates one or more friction-related anomalies.

15. The system according to claim 9, wherein, The computing device analyzes one or more frequency domain parameters in the second control data to determine whether the second control data indicates one or more backspace-related anomalies.

16. The system according to claim 9, wherein, The computing device automatically performs the one or more actions.

17. A computing device for evaluating the condition of a machine having one or more motion axes, the computing device comprising: processor; and A computer-readable storage medium, comprising one or more instructions for causing the processor... Collect initial control data. Analyze the first control data to determine the signature. Collect second control data. The second control data is compared with the signature to identify differences between the second control data and the signature. If the difference exceeds a predetermined threshold, one or more actions are performed to resolve the difference between the second control data and the signature.

18. The computing device according to claim 17, wherein, The processor is further configured to analyze one or more time-domain parameters in the first control data and one or more time-domain parameters in the second control data to determine whether the second control data indicates one or more friction-related anomalies.

19. The computing device according to claim 17, wherein, The processor is further configured to analyze one or more frequency domain parameters in the first control data, and to analyze one or more frequency domain parameters in the second control data to determine whether the second control data indicates one or more backlash-related anomalies.

20. The computing device according to claim 17, wherein, The processor is further configured to automatically perform the one or more actions.

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