Internet of Things (IOT) Applications for Cable Analysis

Abstract

A system, method, and computer-readable medium for a cable analysis and monitoring system are disclosed. In one aspect, the method includes acquiring sensor data from a plurality of sensors, the sensor data comprising a plurality of sensor measurements related to a cable. In one aspect, the method includes storing the sensor data in a database. Each sensor measurement of the plurality of sensor measurements is associated with a group identifier included in a group identifier set. In one aspect, the method includes, for each selected type of one or more selected types of sensor measurements, acquiring a set of time-series sensor measurements of the selected type. In one aspect, the method includes determining one or more warning events associated with the cable based on the set of time-series sensor measurements. In one aspect, the method includes automatically generating one or more corrective actions to correct the warning events.

Description

[Technical Field]

[0001] The subject matter of this disclosure relates generally to the field of line management, and more particularly to managing cables in cable systems by monitoring scheduled maintenance and environmental factors. [Background technology]

[0002] A cable system forms a long-span structure between two objects under tension, typically used for pulling, fastening, installing, carrying, lifting, and climbing. More specifically, cable systems are often used to transport or move heavy loads or materials between two or more objects. A cable is a flexible structure that often supports applied lateral loads through tensile resistance in its components. Cables are used in suspension bridges, tension-moored offshore platforms, power transmission lines, and several other engineering applications. In some cases, the term "cable" is used interchangeably to refer to ropes, cables, and similar components such as suspension slings and round slings. Cables can be made from various metals, such as steel (and its various alloys). Cables may also be made from various man-made and / or man-made fibers. For example, a cable made from one or more synthetic fibers or filaments may be referred to as a "synthetic cable."

[0003] The effectiveness of a cable is often determined by its structural performance, environmental factors, and maintenance. Like most equipment, cables gradually wear, degrading their initial performance more or less rapidly over time. For example, under normal cable use conditions, the cable's properties change over time. Various factors, including the weight of the loads the cable is subjected to, tension, or environmental influences, can cause the cable to deteriorate or lose effectiveness in its structural and operational properties. Structurally degraded cables increase safety risks and reduce the lifespan of the equipment associated with them.

[0004] Therefore, there is a need for a system and method for analyzing and measuring data associated with a cable under normal and high stress conditions to determine whether the cable is satisfactory for continued use in service or whether corrective action is required. [Brief explanation of the drawings]

[0005] The details of one or more aspects of the subject matter described in this disclosure are set forth in the accompanying drawings and the following description. However, the accompanying drawings illustrate only some typical aspects of the disclosure and therefore should not be considered limiting of its scope. Other features, aspects, and advantages will become apparent from the specification, drawings, and claims.

[0006] [Figure 1] FIG. 1 illustrates an example communication diagram of a cable analysis system according to an embodiment of the present disclosure.

[0007] [Figure 2] FIG. 2 illustrates an example dashboard for monitoring the performance of multiple cables in one or more cable systems of a cable analysis system according to an embodiment of the present disclosure.

[0008] [Figure 3] FIG. 3 illustrates an example dashboard alerts module according to aspects of the present disclosure.

[0009] [Figure 4] FIG. 4 illustrates an example interface for assigning tasks using alert status indicators, according to aspects of the present disclosure.

[0010] [Figure 5] FIG. 5 illustrates an example interface for displaying alerts from an alert status indicator according to an aspect of the present disclosure.

[0011] [Figure 6] FIG. 6 illustrates an alert summary module in a cable analysis system according to an embodiment of the present disclosure.

[0012] [Figure 7] FIG. 7 illustrates an example asset view module of a cable analysis system according to an aspect of the present disclosure.

[0013] [Figure 8A] FIG. 8A illustrates an example reporting module that provides performance metrics for a cable or other asset associated with a cable analysis system according to aspects of the present disclosure.

[0014] [Figure 8B] FIG. 8B illustrates an example reporting module that provides information related to retired assets of a cable analysis system in accordance with aspects of the present disclosure.

[0015] [Figure 9] FIG. 9 illustrates a work group interface of a cable analysis system according to an embodiment of the present disclosure.

[0016] [Figure 10] FIG. 10 is a diagram illustrating a task interface of a cable analysis system according to an aspect of the present disclosure.

[0017] [Figure 11] FIG. 11 illustrates an exemplary method in application for cable analysis, according to an embodiment of the present disclosure.

[0018] [Figure 12] FIG. 12 illustrates an exemplary computer system for executing a client application according to aspects of the present disclosure.

[0019] [Figure 13] FIG. 13 is a block diagram illustrating an example of a computing system according to aspects of the present disclosure. Detailed Description

[0020] Various embodiments of the present disclosure are described in detail below. While specific implementations are discussed, it should be understood that such implementations are for illustrative purposes only. Those skilled in the art will recognize that other components and configurations can be used without departing from the spirit and scope of the present disclosure. Accordingly, the following description and drawings are illustrative and should not be construed as limiting. Numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in this disclosure may be to the same embodiment or any embodiment, and such references refer to at least one of the embodiments.

[0021] The term "one embodiment" or "embodiment" means that a particular attribute, structure, or feature described in connection with this embodiment is included in at least one embodiment of the present disclosure. The appearances of the phrase "in one embodiment" in various places throughout the specification do not necessarily all refer to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Additionally, various features are described that may be exhibited by some embodiments and not by other embodiments.

[0022] The terms used herein generally have their ordinary meanings in the art, in the context of this disclosure, and in the specific context in which each term is used. Alternative language or synonyms may be used for one or more of the terms discussed herein, and no special significance should be attached to whether or not a term is recited in detail herein. Synonyms may also be provided for a particular term. The listing of one or more synonyms does not exclude the use of other synonyms. The use of any examples herein, including examples of any terms discussed herein, is merely illustrative and is not intended to further limit the scope and meaning of the disclosure or any exemplified term. Similarly, the disclosure is not limited to the various embodiments set forth herein.

[0023] Without intending to limit the scope of the present disclosure, the following provides examples of instruments, devices, methods, and their related results according to embodiments of the present disclosure. Please note that titles or headings may be used in the examples for the convenience of the reader and do not limit the scope of the present disclosure in any way. Unless otherwise defined, technical and scientific terms used herein have the meanings that are commonly understood by those skilled in the art to which this disclosure belongs. In the event of any conflict, this document, including definitions, shall prevail.

[0024] Additional features and advantages of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the principles disclosed herein. The features and advantages of the present disclosure may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present disclosure will become more apparent from the following description and the appended claims, or may be learned by practice of the principles described herein. overview

[0025] The present disclosure relates to cable system analysis services for analyzing, inspecting, and monitoring cable systems including multiple cables. In some embodiments, the multiple cables may include multiple ropes. In one exemplary implementation, the multiple cables may be synthetic fiber or filament cables (e.g., cables made from one or more synthetic filament materials, such as cables constructed by twisting together multiple synthetic filaments). These include DYNEEMA (ultra-high molecular weight polyethylene), SPECTRA (ultra-high molecular weight polyethylene), TECHNORA (fibers based on terephthaloyl chloride), TWARON® (para-aramid), KEVLAR® (para-aramid), VECTRAN (liquid crystal polymer), PBO (polybenzobisoxazole), carbon fiber, glass fiber, and the like. Modern cables may also be made from older, lower-strength synthetic materials, such as nylon. In the case of high-strength synthetics, the thickness of individual filaments is less than the thickness of a human hair. The filaments have very high tensile strength but are not very stiff. They also tend to have low surface friction. These factors make synthetic filaments difficult to handle during termination and difficult to organize. Hybrid cable designs are also emerging, combining traditional and high-strength synthetic materials. Cables made with synthetic filaments come in a variety of configurations. They often have a protective outer jacket. Because the jacket does not bear significant tensile loads, it may be made of a different material. Most large cables are made as organized groups of smaller cables. These smaller cables are often referred to as "strands." One example is a parallel core of synthetic filaments surrounded by a braided filament jacket. In other cases, the entire cable may be braided. Still other examples include cable configurations such as (1) fully parallel structures enclosed in a jacket made of a different material; (2) helical "twist" structures; (3) more complex structures with multiple helices, multiple braids, or a combination of helices and braids; and (4) hybrid structures that include metallic components.

[0026] The cable system analysis service is configured to ingest data received from multiple sensors in communication with at least one cable (e.g., a synthetic fiber cable), process the data, and make a series of decisions that manage the cable system and corresponding maintenance schedules. The multiple sensors can measure multiple characteristics of the synthetic fiber cable, such as tension, temperature, position, data collection time, vibration, and damping characteristics. In some cases, the systems and techniques described herein relating to one or more synthetic fiber cables may be applied to or utilized with one or more steel cables without departing from the scope of this disclosure.

[0027] In one aspect, a method includes acquiring sensor data from a plurality of sensors, the sensor data comprising a plurality of sensor measurements related to a synthetic fiber cable. In one aspect, the method includes storing the sensor data in a database. Each sensor measurement of the plurality of sensor measurements is associated with a group identifier included in a group identifier set. In one aspect, the method includes acquiring, for each selected type of one or more selected types of sensor measurements, a set of time-series sensor measurements of the selected type. Each stored sensor measurement is associated with a different timestamp. In one aspect, the method includes determining one or more warning events associated with the synthetic fiber cable based on the set of time-series sensor measurements. In one aspect, the method includes automatically generating one or more corrective actions to correct the warning events.

[0028] In another aspect, determining one or more warning events associated with the synthetic fiber cable includes determining one or more baseline fluctuations associated with one or more of the sets of time-series sensor measurements; analyzing a recent portion of the sets of time-series sensor measurements against the determined one or more baseline fluctuations and one or more thresholds; and identifying one or more deviations or anomalies in the analysis of the recent portion of the sets of time-series sensor measurements and generating a warning event based thereon.

[0029] In another aspect, the one or more thresholds include a predetermined threshold determined based on one or more user inputs or a predetermined threshold determined based on an analysis of the set of time-series sensor measurements against a ground truth labeled dataset.

[0030] In another aspect, the method further includes generating a ground truth labeled dataset based on obtaining historical results data associated with one or more reference cables, obtaining reference measurements associated with the one or more reference cables, and generating one or more ground truth labels based on correlating historical results of the historical results data with one or more features of the reference measurements during model refinement.

[0031] In another aspect, the method further includes acquiring real-time sensor data from the plurality of sensors, the real-time sensor data including a plurality of real-time sensor measurements associated with the synthetic fiber cable; determining one or more ongoing warning events associated with the synthetic fiber cable based on the set of time-series sensor measurements and the real-time sensor data; and automatically generating one or more alerts indicating the one or more ongoing warning events associated with the synthetic fiber cable.

[0032] In another aspect, the one or more selected types of sensor measurements are based on receiving a user selection for a particular cable from a plurality of cables represented in the database, determining a plurality of available sensor measurement types corresponding to stored sensor measurements associated with the particular cable, and obtaining the one or more selected types of sensor measurements for the particular cable as selected by the user from the plurality of available sensor measurement types.

[0033] In another aspect, the one or more selected types of sensor measurements are determined based on one or more user inputs in a user interface, and the stored sensor measurements of each selected type are obtained based on correlating each user input with a corresponding group identifier used in the database.

[0034] In another aspect, the one or more selected types of sensor measurements are obtained based on an automatically generated work group of associated cables and sensors, the automatically generated work group including the one or more selected types of sensor measurements, the automatically generated work group being determined based on identifying one or more types of sensor measurements that correlate with a given cable.

[0035] In another aspect, the one or more selected types of sensor measurements are associated with the synthetic fiber cable or with a work group that includes the synthetic fiber cable and one or more physical assets different from the synthetic fiber cable.

[0036] In another aspect, the sensor data includes a first subset of sensor data including sensor measurements representative of physical properties of the synthetic fiber cable, the first subset of sensor measurements being obtained from sensors associated with the synthetic fiber cable, and a second subset of sensor data including sensor measurements corresponding to an ambient environment of the synthetic fiber cable.

[0037] In another aspect, at least one sensor measurement of the first subset is obtained using a sensor coupled to the synthetic fiber cable.

[0038] In another aspect, the sensor coupled to the synthetic fiber cable includes a fiber optic sensor.

[0039] In another aspect, at least one sensor measurement of the first and second subsets of sensor data is obtained using a battery-powered sensor, the battery-powered sensor including a transceiver that communicates with a receiver associated with the database.

[0040] In another aspect, one or more of the plurality of sensor measurements are obtained using a non-contact sensor coupled to the synthetic fiber cable or associated with an environment surrounding the synthetic fiber cable.

[0041] In another aspect, at least a portion of the sensor data is obtained as sensor measurements transmitted intermittently by intermittently reporting sensors included in the plurality of sensors.

[0042] In another aspect, the method further includes configuring the intermittent reporting sensor with one or more predetermined reporting thresholds, the intermittent reporting sensor remaining in a low power mode and not reporting when collected sensor data is below at least one of the predetermined reporting thresholds, and the intermittent reporting sensor exiting the low power mode and reporting when collected sensor data exceeds at least one of the predetermined reporting thresholds.

[0043] In another aspect, at least one sensor measurement of the second subset is obtained from a sensor associated with an asset located in the surrounding environment of the synthetic fiber cable.

[0044] In another aspect, the second subset of sensor data includes environmental kinematic measurements or baseline motion information determined for the surrounding environment of the synthetic fiber cable.

[0045] In another aspect, the method further includes performing motion compensation on the first subset of sensor data associated with physical properties of the cable, the motion compensation being based on the environmental kinematic measurements or the baseline motion information associated with the surrounding environment of the synthetic fiber cable.

[0046] In another aspect, performing the motion compensation includes generating a refined version of the first subset of sensor data that removes the baseline motion information associated with the surrounding environment of the synthetic fiber cable. Illustrative Embodiments

[0047] Additional features and advantages of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the principles disclosed herein. The features and advantages of the present disclosure may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present disclosure will become more apparent from the following description and the appended claims, or may be learned by practice of the principles described herein.

[0048] Cable systems can be incorporated into a variety of environments, not just maritime activities. Ship crews often utilize cable systems during berthing, anchoring, towing, and rigging operations, which are essential for securing marine mobile objects. Cable systems, which contain multiple cables, must be inspected and monitored regularly to ensure they are in working order, functioning properly, and free of damage or deterioration. It is also necessary to ensure that replacement is not required, so that the objects secured by the cable system are not exposed to safety risks or loss.

[0049] In some embodiments, various cable inspections are performed manually or visually, involving crews physically handling and inspecting the cable system and attempting to detect or identify structural damage or anomalies in one or more cables within the cable system. These inspections are typically directed by manuals and / or procedures developed by the cable system manufacturer or operator, which provide instructions for conducting thorough inspections. However, despite available procedural approaches, visual inspection of cables remains a highly subjective process and susceptible to various sources of error. Visual inspections may miss (e.g., fail to identify) some types of damage that may occur to a cable. When subjected to damaging conditions, cable damage can occur in a variety of ways, and in some cases, it may be difficult or impossible to determine what has happened to the cable. For example, if a cable (e.g., a synthetic fiber cable) is exposed to damaging or adverse environmental conditions, the condition may not be visually indicated by either a visual inspection of the exterior of the cable and / or a visual inspection of the interior of the cable strands. Errors during visual inspection of cables may result in cables with undetected damage or other significant problems passing the inspection. In many cases, the uncertainty associated with visually inspecting cables can result in cables being retired too early (e.g., a conservative approach, which can create operational and financial challenges) or, conversely, cables being retired too late (e.g., an aggressive approach, which can pose significant safety risks).

[0050] In some embodiments, testing equipment may be implemented within a cable system. For example, a tension meter may be used to measure the tension in the cables within the cable system and determine whether the tension is above or below an acceptable level for continued operation. In other embodiments, various forms of non-destructive and / or destructive testing may be performed on the cables. However, many existing forms of testing for cables and cable systems require the cables to be taken out of service before the tests can be performed, which may be undesirable or even impossible. For example, many forms of testing are not feasible for cables that are under constant tension (e.g., cables that are under tension throughout their entire life from installation to decommissioning).

[0051] It is worth noting that visual inspections and various other existing cable testing approaches merely provide point-in-time snapshots and do not predict the overall condition of a cable, nor are they indicative of the conditions the cable has been subjected to. For example, cables may degrade and wear nonlinearly over time (e.g., wear more rapidly near the end of a cable's life), so passing a visual inspection or traditional cable testing procedures only indicates (at best) that the cable is in acceptable condition as of the present. Therefore, there is a need for systems and techniques that can address these and other issues without falling prey to the deficiencies in predicting future performance common to existing approaches.

[0052] There is also a need for systems and techniques that can be used to analyze one or more cables with a more complete understanding of the services the cable has undergone over time. For example, as disclosed herein, a cable's service history can be a strong predictor of the cable's future performance. A cable's service history may include the physical services performed by the cable (e.g., load duration, load magnitude, shock load events, etc.) and / or the cable's environmental history (e.g., temperature, location, etc.). As discussed above, existing approaches are based on various inspections or tests that provide a snapshot of the cable's condition corresponding to a discrete point in time, and therefore do not reflect or indicate the impact of the cable's cumulative service history.

[0053] Therefore, there is a need for systems and techniques that can reliably obtain information indicative of the services a cable has performed over time and the environmental exposures it has been subjected to over time.As described in more detail below, there is a further need for systems and techniques that can more accurately predict future cable performance based on integrating and analyzing cable information obtained over time and from multiple different data sources or modalities.

[0054] As mentioned above, ensuring the structural and physical properties of cable systems remain intact is paramount to ensuring the safety of fixed objects. Existing technologies focus on examining the current condition of cables, but do not provide an indication of the conditions to which the cables will be subjected. This leaves a significant gap in understanding the condition of cables, as test methods are limited in their ability to infer the conditions to which the cables will be subjected. Furthermore, field testing systems are cumbersome and often impractical, as they require the systems to be taken out of service while tests are performed.

[0055] The disclosed technology addresses a need in the art for a cable system analysis service for analyzing, inspecting, and monitoring a cable system including multiple cables. As described below, embodiments of the present disclosure enable a cable analysis service to ingest data received from multiple sensors in communication with the cables, process the data, and make a series of decisions that manage the cable system and corresponding maintenance schedules. The multiple sensors can measure multiple characteristics of the cables, such as tension, temperature, location, and time of data collection.

[0056] 1 illustrates an exemplary communication diagram of a cable analysis system 100 according to an embodiment of the present disclosure. The cable analysis system 100 is configured to manage cables (e.g., cables associated with or registered with the cable analysis system 100) to manage their lifespan and to develop maintenance and action plans to maintain structural characteristics of the cables under management above predetermined thresholds. The cable analysis system 100 can include a central network 102 configured to maintain communication between one or more systems: the cable analysis system 100, including a database 104, a cable system 106, a sensor module 108, and an internal computing system 112.

[0057] The sensor module 108 of the cable analysis system 100 includes multiple sensor systems 110a-110b (first sensor system 110a-Nth sensor system 110b). For example, the sensor systems 110a-110b can be located within (or on, around, or near) one or more of the multiple cables or at multiple locations along the entire cable system 106. The sensor systems 110a-110b can be of different types and can be located around the cable system. For example, the first sensor system 110a can be a tension meter, and the Nth sensor system can be a temperature sensor. Other examples of sensor systems include location service sensors such as potentiometric position sensors, time monitoring sensors such as timing devices, camera sensor systems, lidar sensor systems, radar sensor systems, global positioning system (GPS) sensor systems, inertial measurement units (IMUs), infrared sensor systems, laser sensor systems, sonar sensor systems, etc.

[0058] The sensor modules 108 can communicate with an internal computing system 112 via the central network 102. The internal computing system 112 can communicate with the sensor systems 110a-110b and the database 104. The internal computing system 112 includes at least one processor and at least one memory having computer-executable instructions executed by the processor. The computer-executable instructions can configure one or more services responsible for controlling the cable analysis system 100, communicating with the database 104, receiving input regarding the sensor systems 110a-110b and data collected by an operator of the system, and logging measurements.

[0059] The internal computing system 112 may include a control service 114 configured to control the operation of the cable system 106 and the sensor module 108. The control service 114 receives sensor signals from the sensor systems 110a-110b and communicates with other services of the internal computing system 112 to enable the operation of the cable analysis system 100.

[0060] The internal computing system 112 may also include an analytics service 116 that receives data from the sensor modules 108 and analyzes the data to train or evaluate machine learning algorithms for managing the cable system 106. The analytics service 116 may also perform analytics on the data from the sensor systems 110a-110b to correlate the data with one or more warning events associated with the cables of the cable system 106 reported by the sensor modules 108. Once the data from the sensor modules 108 is processed by the analytics service 116, metrics from the sensor systems 110a-110b and / or the machine learning algorithms may be stored in the database 104. For example, the database 104 may hold raw sensor data obtained from the sensor systems 110a-110b, may hold processed sensor data generated by processing the raw sensor data using the analytics service 116, and / or may hold processed sensor data (and associated metrics) generated by providing the sensor data to one or more machine learning algorithms. In one exemplary embodiment, the information stored in the database 104 may be used to determine one or more warning events associated with a particular cable. In some aspects, the systems and techniques described herein are also used to automatically generate one or more corrective actions to correct an alert event, as described in more detail below.

[0061] The internal computing system 112 may also include a communication service 118. The communication service 118 may include both software and hardware elements for transmitting and receiving signals to and from the internal computing system 112 over the network 102. The communication service 118 may be configured to transmit information wirelessly over the network 102, for example, via an antenna array providing personal cellular communications (e.g., Long Term Evolution (LTE), 3G, 5G, etc.), or via a wired or wireless local area network (LAN).

[0062] In some embodiments, one or more services of the internal computing system 112 are configured to send and receive communications with the cable system 106 for reasons such as reporting data for training and evaluation of machine learning algorithms, requesting maintenance, sending warning events, or generating one or more corrective actions to correct warning events.

[0063] The internal computing system 112 may also include an instruction service 120 for sending instructions regarding the operation of the cable system 106 and the sensor module 108. For example, in response to the output of the analysis service 116 or the user interface service 122, the instruction service 120 may prepare instructions for one or more sensor systems 110a-110b of the sensor module 108 and the cable system 106.

[0064] The user interface service 122 is configured to present to an operator of the cable analysis system 100 metrics, data indicators, charts, and sensor readings reported from the cable system 106. The user interface service 122 can also receive input instructions from the operator that can be sent to the cable system 106 or the sensor module 108.

[0065] The data presented in the user interface service 122 may be depicted through various dashboard visualizations that present captured and / or processed data according to sensor readings from the sensor systems 110a-110b. The captured data may include cable data collected from sensors installed or embedded in or around the structure of the cable, located near or associated with the cable, and via additional module devices or systems. In some embodiments, multiple sensor systems 110a-110b may be installed on each cable of the cable system 106.

[0066] The captured data may also include data about the object to which the cable system 106 is associated or installed. In some embodiments, the cable system 106 may be installed on a winch, a crane, or a marine vehicle. Depending on the associated object, the sensor systems 110a-110b may be configured to isolate the motion of the object, such as a marine vessel or a crane, from the data collected from the cable. For example, the sensor systems 110a-110b may be configured to detect and report data only if motion greater than the reference vessel motion is detected. Otherwise, the sensor systems 110a-110b may be programmed not to transmit or report data and to remain in a low-power mode.

[0067] The captured data may also include environmental data. The environmental data may include climate, temperature, weather, or other environmental conditions to which the cable system 106 is exposed. In some embodiments, a sensing box may be installed on an object and associated with the cable system 106 to collect the environmental data. The environmental data collected from the sensing box provides context to aid in interpreting the data collected from the sensor module 108 and may be integrated with the sensor readings collected from the sensor module 108. Integrating the environmental data may establish steady-state or baseline information related to normal environmental conditions, from which multiple determinations may be made that indicate how changes in the environment affect the structural integrity of the cable system 106.

[0068] In some embodiments, the detection box may include one or more fiber optic sensors, battery-powered intermittent reporting sensors, or passive sensors that may be coupled to an interrogator for intermittent reporting, triggering detection, or a combination thereof.

[0069] Therefore, a dashboard can be utilized to visually present the data collected according to each of the above-described embodiments.

[0070] FIG. 2 illustrates an example dashboard 200 that can be used to monitor the performance of multiple cables in one or more cable systems 106 of a cable analysis system 100 according to embodiments of the present disclosure. Dashboard 200 can provide various indicators that provide information related to the various cables in the cable system. The indicators (and associated information) may be automatically and / or dynamically generated and / or presented by dashboard 200, for example, while the various cables in the cable system are in use. Dashboard 200 is presented on a computing device or a graphical user interface accessible by various user devices and computing devices. The cable performance indicators presented by dashboard 200 can be replaced with additional data metrics for further analysis and / or used to train one or more machine learning networks or artificial intelligence models.

[0071] As shown in FIG. 2 , dashboard 200 can include a list of related assets 202, including a set of cables and the associated location of each cable in the set of cables. In some embodiments, a unique asset ID can be used to display each cable in the list of related assets 202. For example, a unique asset ID can be assigned to or associated with each cable during the process of installing or registering each cable in the cable analysis system described herein. Thus, a user of the cable analysis system (e.g., a user associated with a particular user account that has permission to view and manage one or more cable systems having one or more lists of related assets 202) can manage multiple cable systems within dashboard 200. As in the example shown in FIG. 2 , dashboard 200 can allow a user to view an asset identifier, the asset location, the asset status (e.g., whether the asset is operational or non-operational), the battery life of one or more sensors or sensor systems associated with the asset, and the date of the asset's most recent inspection, among other information related to a cable or other asset.

[0072] Dashboard 200 may also include an app usage indicator 208. App usage indicator 208 may include the total number of users authorized to access one or more cable systems managed by the cable analysis system, or more specifically, the number of users authorized to access cable analysis system 100 and associated dashboards (e.g., dashboard 200). In some embodiments, the number of users indicated in app usage indicator 208 may correspond to the number of unique user accounts registered or provided in association with a particular set of assets or cable systems. App usage indicator 208 may further provide an administrative user with usage data indicating the total amount of time registered users (and / or each user therein) accessed dashboard 200 and / or used cable analysis system 100.

[0073] Dashboard 200 may further include alert indicators 210. Alert indicators 210 may provide a user of dashboard 200 with multiple alerts related to the overall management and operation of assets included in the list of related assets 202. For example, an alert may indicate to a user that cable system 106 (e.g., one or more specific cables registered with cable analysis system 100 and associated with the user account) requires maintenance, inspection, or attention based on a change in condition or other triggered alert.

[0074] One or more alerts in the alert indicator 210 may be triggered based on information also indicated in a status module 204, which includes multiple status indicators 206a-206c. Accordingly, the status module 204 may include a graphical view of the asset status managed by the cable analysis system and various corrective actions to be actively monitored to ensure operational life based on the status indicators 206a-206c. Specifically, the status module 204 may include a status update indicator 206c that quantifies the number of cable systems 106 managed by the cable analysis system 100 and the percentage of managed cable systems currently associated with a particular status. For example, the status update indicator 206c may indicate the percentage of active, inactive, and / or retired assets indicated in the list of related assets 202. The status module 204 may further include a maintenance status indicator 206b that indicates, for example, the percentage of maintenance tasks that have been completed. The status module 204 may further include an inspection status indicator 206a. The inspection status indicator 206a may indicate the percentage of assets in the list of related assets 202 that require inspection. In some embodiments, the inspection status indicator 206a may indicate the percentage of assets that have successfully completed a scheduled inspection or that have undergone some form of scheduled inspection.

[0075] In some embodiments, various interfaces of dashboard 200 may provide historical comparisons of sensor readings collected from various cable system sensor systems. These interfaces are described in more detail below in connection with FIGS. 3-5. As described above, with respect to dashboard 200, dashboard 200 may be viewed as providing an operator (e.g., a user of cable analysis system 100) with an intuitive, dynamic, and effective visual representation for monitoring a cable system, cables, assets, etc. The visual representation of dashboard 200 may be automatically updated substantially in real time (e.g., as new sensor data is streamed or received by cable analysis system 100), periodically updated at predetermined intervals (e.g., time intervals, thresholds for the amount of sensor data received, thresholds for the amount of change in one or more sensor data measurements, etc.), and / or updated on-demand. In each example, the visual representation of dashboard 200 may be updated based on incorporating additional (e.g., new or newly acquired) sensor data measurements and associated information derived from sensor systems 110a-110b. 5 provides a table listing various sensor-based performance indicators (e.g., including temperature 510, pressure 512, wear 514, and tension 516), which may be updated according to any of the approaches described above as additional or changed sensor data (and / or processed sensor data) is acquired over time. As described in more detail below with specific reference to FIG. 5, the dashboard may further provide, for each performance indicator, an average sensor reading (e.g., over a predetermined period of time) 518 and / or a real-time (e.g., point-in-time) sensor reading 520.

[0076] Referring now to FIG. 3 of the present disclosure, an alert module 300 of the dashboard 200 is illustrated in accordance with an aspect of the present disclosure. The alert module 300 is configured to notify users of each cable system (e.g., each cable system included in or managed by the cable analysis system) of necessary proactive or corrective actions. For example, the actions or corrective actions may be referenced with respect to the specific cable to which they correspond or for other reasons. The alert module may notify the user through multiple alert status indicators 302, including alerts generated over a period of time or during various predetermined or pre-planned operations. The alert status indicators 302 may indicate to the user the severity of the alert associated with the asset, which may include a low alert status, a medium alert status, a high alert status, or a critical alert status. The alert status indicators 302 may further indicate whether the cable system associated with the alert requires calibration. The alert status indicators 302 may also notify the user of the cause of the alert. For example, the cause of the alert may be identified as one or more conditions, parameters, or occurrences that caused the cable analysis system to generate one of the corresponding alert status indicators 302. In some cases, if the alert status indicators 302 include suggestions for corrective actions (e.g., actions to address the alert), the cause of the alert may be determined based at least in part on the type of corrective action suggested for the alert.

[0077] The alert status indicators 302 and data associated with each alert of the alert status indicators 302 may be grouped into various work groups in the work group module 304. In particular, with respect to the alert status indicators 302, the work group module 304 may be configured to organize individual alerts according to the asset group assigned to the particular cable / asset for which the individual alert was generated. In general, the alert status indicators 302 may include alerts assigned to particular response teams or repair users and / or alerts grouped based on the region, cable system, or organizational project supported by the cable analysis system. The work groups may also include tasks assigned by an administrative user in the task module 306. The task module 306 may be configured to automatically assign tasks based on the alert status from the alert status indicators 302 and / or based on a determination of the cause (or possible cause) for which the alert was triggered. The task module 306 may also be manually configured to assign tasks to one or more work groups based on the type of alert, the type of corrective action required, and the group required to implement the action required to correct the alert of the alert status indicator 302.

[0078] Based on tasks assigned in the task module 306, alerts indicated by the alert status indicators 302, and workgroups assigned tasks in the workgroup module 304, the asset activity summary 308 may provide a current overview of the overall or global status of multiple cables, cable systems, and / or other assets registered and managed in the cable analysis system 100. For example, in one illustrative embodiment, the asset activity summary 308 may show the number of in-service assets, out-of-service assets, assets needing repair, repaired assets, or retired assets. Additionally, the asset activity summary 308 may include one or more visual representations showing trends in the displayed numbers. For example, each quantity may be displayed with an up or down arrow to indicate an increasing or decreasing trend in the displayed quantity over time. As shown in FIG. 3 , the number of “in-service” assets is shown with an up arrow, indicating an increase in the total number of “in-service” assets over time, and the number of “out-of-service,” “repaired,” and “retired” assets is shown with a down arrow, indicating a decrease in the number of each of the three types of assets over time.

[0079] FIG. 4 illustrates an example interface for assigning tasks based on one or more automatically generated alert status indicators 302, according to aspects of the present disclosure. An administrative user may assign tasks via the tasks module 306 through the selection of a task assignment user interface (UI) element 402. Selecting the task assignment UI element 402 enables the user to interact with a task assignment interface 404. As shown in FIG. 4 , selection of the task assignment UI element 402 can overlay the task assignment interface 404 on a dashboard interface that includes the task assignment UI element 402. Upon selection, the task assignment interface 404 may be configured to receive one or more user inputs from a user, for example, based on a corresponding series of prompts and / or user input fields presented in the task assignment interface 404. In some aspects, the task assignment interface 404 can receive user input related to the assignment of tasks for a particular alert indicated in the alert status indicator 302.

[0080] In some aspects, the task assignment interface 404 may be configured to automatically populate (e.g., pre-populate) a corresponding asset or asset ID based on the asset highlighted or selected when a user initially selects the task assignment UI element 402. In some embodiments, the task assignment interface 404 may automatically determine information for populating corresponding user input fields, including, but not limited to, the selected asset, the date the task was performed, the assigned task, the work group or individual to whom the task was assigned, and / or the priority of the task, among other information. For example, one or more rules may be pre-defined to specify conditions for automatically populating the user input fields of the task assignment interface 404. In one illustrative example, the "Task" field may be automatically populated based on the type of asset (e.g., steel cable vs. rope cable), the current date and task history for the selected asset (e.g., comparing the current date with scheduled dates for inspection tasks, repair tasks, etc.), and / or unique work group-specific rules for task assignment. For example, within a given work group, all tasks for a particular asset ID may be automatically assigned to a particular individual and may be given a particular priority, etc.

[0081] In some embodiments, task priorities may be automatically determined based on the type of task assigned to the asset and / or the workgroup with which the task is associated. In other embodiments, a workgroup or individual may be automatically assigned based on the assigned task, along with a priority level normally assigned to that workgroup or individual. In some embodiments, a machine learning algorithm may be used to automatically assign workgroups and priorities based on the type and / or level of the alert in the alert status indicator 302 and the workgroup to which it needs to be assigned. The machine learning algorithm may utilize the past history of the set of alerts in the alert status indicator 302 used to train the machine learning algorithm to automatically assign tasks in the task module 306.

[0082] 4, before assigning a task, the administrative user may choose to review the alert and determine if there is additional information or data that should be reviewed before determining the necessary action for the alert. Thus, the administrative user may select the alert from the alert status indicator 302 to review this data.

[0083] 5 illustrates an example interface that can be used to review alerts from the alert status indicators 302 according to aspects of the present disclosure. An administrative user or authorized user can select an alert from the alert status indicators 302 to be displayed on a graphical user interface of a user device, computing device, or other user equipment. Upon selecting a particular alert, an alert summary 502 may display the current and historical data that led to the particular alert being triggered and included in the alert status indicators 302.

[0084] For example, the displayed data may include a list of one or more past inspections 504 that have been performed on a selected asset (e.g., a particular cable in a cable system). The past inspections 504 of the cable system may include a historical reference to inspections performed over a period of time, and may further include the date and time of inspections performed in response to alerts previously indicated by the alert status indicator 302.

[0085] The displayed data may include cable system condition data 506, including, but not limited to, cable or asset performance indicators (e.g., average and instantaneous sensor readings), troubleshooting history, identification information, and past performance history. Performance data may include data related to temperatures affecting the cable system, pressures experienced, wear data for cables within the cable system, tensions experienced by cables within the cable system, etc. Performance data may be presented as raw sensor data and / or may be derived from the raw sensor data (e.g., using one or more machine learning models, using the analytics service 116 of FIG. 1 ).

[0086] Accordingly, cable analysis system 100 and the various dashboards and user interfaces described herein can be used to provide an operator with a visual representation of the monitored performance and information related to one or more cables, cable systems, and / or other monitored assets registered with cable analysis system 100. As previously described, cable analysis system 100 and the various dashboards and user interfaces described herein can be configured to update automatically, periodically, and / or on-demand. As shown in FIG. 5 , some or all of the sensor data (raw sensor data or processed sensor data) may be presented using one or more graphical interfaces. For example, cable system status 506 includes a table listing a set of performance indicators, including temperature 510, pressure 512, wear 514, and tension 516. Cable system condition 506 can further provide average calculations 518 or real-time calculations 520 for each performance indicator.

[0087] In one exemplary embodiment, an operator can use the cable system status user interface element 506 (and the data presented therein) to make various determinations related to cable performance, risk factors to the cable, etc. For example, factors such as temperature, pressure, wear, and tension can all cumulatively affect the structural integrity of a cable over time. In other words, an operator may be more interested in the amount of time a cable has been under tension (or above a predetermined tension threshold) or the number of temperature cycles the cable has experienced between upper and lower temperature thresholds than in instantaneous information. For example, an operator can review data from sensor systems 110a-110b in dashboard 200 and determine that adjustments are needed to the environment or characteristics affecting the cable system.

[0088] In some embodiments, the determination that an adjustment can or should be made may be generated automatically by the internal computing system 112 of the cable analysis system 100, for example, based on one or more predetermined thresholds. The predetermined thresholds may be pre-set by an operator of the cable analysis system 100 and / or may be dynamically determined based on machine learning algorithms that determine the optimum conditions for the cable system based on one or more of environmental data, sensor data from the sensor system, and the type of cable installed in the cable system.

[0089] Thus, as cable analysis system 100 analyzes data received from the sensor system, the machine learning algorithm may be trained to set new thresholds based on the age of the cable, the type of cable installed, and historical data established by dashboard 200. The training data may further incorporate performance data of the performance indicators over a period of time, which may be programmed and selected based on weekly, monthly, or yearly performance of the sensor data collected from the cable system.

[0090] In some embodiments, the displayed data may further include graphical representations or performance charts, such as first performance chart 508a and second performance chart 508b shown in FIG. 5, each of which includes a graphical representation of a time series of various performance indicators monitored by the cable analysis system.

[0091] For example, first performance graph 508a shows a time history of temperature and pressure sensor data associated with a selected asset. Here, a time history over a one-week period is shown, but it should be noted that various other time intervals may also be utilized. The time history of sensor data may be generated based on sensor data obtained using one or more sensors or sensor systems configured to collect temperature 510 and pressure 512 data associated with a selected asset (e.g., a cable). The historical sensor data over the time history interval may be obtained as stored sensor data from one or more databases associated with cable analysis system 100. The collection of data from temperature 510 and pressure 512 in first performance graph 508a may include temperature fluctuations (e.g., degrees Fahrenheit) and pressure fluctuations (e.g., pounds per square inch (PSI)) over a period of time (e.g., day, week, month, etc.).

[0092] The second performance graph 508b shows a time history of wear and tension data associated with a selected asset (e.g., cable). Here, the time history is again shown over a one-week period, although it should be noted that various other time intervals may also be utilized. The time history of sensor data may be generated based on sensor data obtained using one or more sensors or sensor systems configured to collect wear 514 and tension 516 data associated with the selected asset (e.g., cable). The data collected in the second performance graph 508b may be used to display a correlation between wear experienced by cables in the cable system compared to tensions experienced by the cables during operation or under various load conditions.

[0093] In some embodiments, the cable analysis system 100 may automatically make a determination from the data, or an operator may manually make a determination based on an association between at least two performance indicators. The determination may be made based on obtaining, for each selected type of sensor measurement, a set of time-series sensor measurements corresponding to the selected type. Each stored sensor measurement is also associated with a different timestamp. A warning event associated with the cable can be determined from an analysis of the timestamps. In some aspects, the warning event may be used or configured to automatically generate one or more corrective actions for an operator or the cable system to correct the warning event.

[0094] In some embodiments, patterns may be determined from determining associations between performance indicators and sensor measurements. Patterns may be determined based on time-independent and time-dependent correlations associated with cable usage. In some aspects, patterns may be determined based at least in part on analyzing raw or processed sensor data using one or more trained machine learning models. For example, one or more trained machine learning models may be trained using training data that includes past sensor measurements associated with an asset and corresponding outcomes or events that occurred on the asset. For example, for a particular asset or type of asset (e.g., a particular cable or type of cable), historical temperature, pressure, wear, tension, etc. data may be obtained and labeled with various outcomes or events that occurred over the cable's lifetime. In one illustrative embodiment, the training data may be labeled with outcomes, such as cable failure, cable overload, and various cable degradations. The sensor measurements included in the training data and the labeled outcomes associated with the asset may be associated with a timestamp, allowing sensor measurements corresponding to a time interval prior to the occurrence of a labeled outcome (e.g., cable failure) to be identified and used to train the machine learning model. In some embodiments, the sensor measurements and performance indicators may be used by the systems and techniques described herein to determine a usage level associated with an asset (e.g., a cable). The usage level may then be compared to various thresholds generated from judgments and historical data to determine whether a threshold has been met, whether a warning event has been generated, and whether and when corrective action is required.

[0095] In some embodiments, warning events may be customized by configuring one or more parameters and / or alerts used to trigger system actions (e.g., warning events). For example, the customized configuration may include predetermined alarms, warnings, or thresholds that trigger the cable analysis system 100 to generate warning events and corrective actions to address changes in the cable's structural characteristics that are deemed outside of normal or safe parameters. In some embodiments, the sensor data may be used to automatically generate one or more predictions for the asset (e.g., cable) associated with the sensor data. The predictions may include determining the remaining life of the cable based on observed wear and environmental conditions affecting the cable system. Thus, based on the predictions, the first and second performance graphs 508a and 508b may further display warning lines and thresholds that indicate harmful thresholds that may trigger a damage event that could jeopardize the integrity of the cable system. In some embodiments, multiple reports may be generated that include sensor data from the sensor system and predictions for the life of one or more cables.

[0096] FIG. 6 illustrates an example of an alert summary module 600 that may be included in a cable analysis system 100 according to aspects of the present disclosure. The alert summary module 600 may include a historical list of alerts 602 received for various assets registered with the cable analysis system 100. In some embodiments, the historical list of alerts 602 may represent alerts received for a particular set of assets within a predetermined period of time that are monitored and tracked by the cable analysis system 100. In some embodiments, the cable analysis system 100 may be configured to receive data from all monitored cable systems and use that data to determine whether to trigger an alert based on whether the data levels reach one or more thresholds. When a threshold is met, the cable analysis system 100 may determine whether an alert was indeed triggered and then update the list of alerts 602 in the alert summary module 600.

[0097] In some embodiments, the list of alerts 602 may be sorted or filtered according to a particular location in the cable system, the type of alert, a particular suggested corrective action associated with the alert, and a date range in which the alert may have occurred. In some embodiments, the list of alerts 602 may be sorted or filtered using various parameters. For example, the list of alerts may be categorized into a set of active alerts 604 and a set of past alerts 606, among various other classification and / or filtering schemes that may be utilized within the scope of the present disclosure. Within each category, an activity summary 608 may be provided for all alerts included in that category. For example, upon selecting active alerts 604 or past alerts 606, the interface may display the total number of alerts in the category, the number of closed alerts, the number of alerts indicating that the cable system needs to be calibrated, the number of alerts indicating that the cable system needs to be inspected, the number of alerts indicating that the cable system needs repair, and the number of alerts indicating that one or more sensors associated with the cable system may have a low battery level. Each status indication may be associated with a trend indicator that indicates whether the number of each type of alert is increasing or decreasing over a given period of time. 6, the alert status indicators "Total," "Finished," "Check," and "Low Battery" are showing an increasing trend, as indicated by the upward arrows associated with each of these four alert status indicators. The alert status indicators "Calibrate" and "Repair" are showing a decreasing trend, as indicated by the downward arrows associated with each alert status indicator. In some embodiments, alerts received by the alert summary module 600 may be used to trigger or perform additional automated and / or manual analysis to determine corrective actions to be taken to address the alert, as well as to determine the work group to which the alert should be assigned.

[0098] 7 illustrates an example asset view module 700 of a cable analysis system 100 according to an embodiment of the present disclosure. The asset view module 700 can be configured to provide an interface that shows an overview of the overall health of an asset (e.g., a cable or cable system) being monitored and tracked by the cable analysis system 100. For example, the asset view module 700 may be used to provide more detailed reports regarding past and current information obtained for a selected asset and / or to provide more detailed reports regarding past and current derived information (e.g., processed information or predicted information) determined for a selected asset.

[0099] For example, asset view module 700 may be displayed in the user interface of cable analysis system 100 in response to a user's selection of a particular asset. As shown in FIG. 7 , a particular asset may be received by reference to a list of assets available for detailed inspection in asset view module 700 (e.g., the left asset list column shown in the example interface of FIG. 7 ). In response to a user selecting a particular asset, asset view module 700 may display historical data including the asset's past inspection history. In some embodiments, asset view module 700 may display past inspection history 504, as described above with respect to FIG. 5 . The asset view module may additionally or alternatively display cable system status information that is the same as or similar to cable system status information 506 of FIG. 5 . Thus, data related to past inspections of an asset can provide data indicators related to corrective actions that may be manually or automatically assigned to a work group associated with the asset. The cable system status 506 can provide data that provides real-time calculations 520 and average calculations 518 to assist in validating received alerts and assessing the type of corrective action needed to address concerns raised by temperature 510, pressure 512, wear 514, and tension 516.

[0100] The asset view module 700 may also include graphical views of the data included in the cable system status 506 in a first performance chart 508a and a second performance chart 508b. The first performance chart 508a and the second performance chart 508b each provide a historical view of the data over multiple days, allowing various correlations between the data to be identified in relation to various environmental and workload effects.

[0101] The asset view module 700 may also include a selection pane 702 that allows a user to select and access detailed information about various other assets with which the user is associated. For example, multiple asset summaries may be viewed and selected in the selection pane 702. In some embodiments, the selection pane 702 may be pre-populated based on assigned work groups and authorization or validation of the work group's access credentials. Thus, users assigned to a work group can view all alerts assigned to that work group, assign tasks, review data related to alerts requiring repair, and monitor the current status of other assets assigned to the work group.

[0102] FIG. 8A illustrates an example of a reporting module 800 that may be included in cable analysis system 100 and / or used to generate one or more dashboards or other user interfaces associated with cable analysis system 100. In one exemplary embodiment, reporting module 800 may be used to generate a dashboard view including one or more performance metrics for an asset (e.g., a cable or cable system) registered with cable analysis system 100. Reporting module 800 may be configured to display the report in a graph view of the user interface. The graph view may provide a visual representation of current and / or historical conditions experienced by an asset (e.g., a cable) monitored by cable analysis system 100. The conditions may include, but are not limited to, various sensor measurements such as temperature, pressure, wear, tension, etc. The conditions may additionally or alternatively include one or more processed or predicted values ​​that are determined at least in part based on the sensor measurements. In some aspects, the sensor measurements used to generate the graphical representation of the reporting module 800 may be the same as or similar to the cable system status information 506 and / or the first performance chart 508a and second performance chart 508b, as shown in FIG. 5 .

[0103] 8A , a first data index 806 may be generated for a particular asset and may be depicted as a time history of a first type of sensor reading (shown here as temperature). A second data index 808 may be generated for a particular asset and may be depicted as a time history of a second type of sensor reading that is different from the first type. Greater or lesser amounts of different data indexes (e.g., different sensor reading types) may be overlaid on the graphical representation generated by the reporting module 800 without departing from the scope of the present disclosure. The selection of overlaying different data indexes on the same graphical representation (e.g., graphical representation 802) may be selected by a user and / or automatically determined by the cable analysis system 100.

[0104] 8A 。 The warning threshold 804 may indicate an upper or lower boundary of a safe or acceptable sensor reading, and a sensor reading exceeding the warning threshold 804 may indicate an unsafe or undesirable operating condition of the associated asset (e.g., a cable). For example, the warning threshold 804 may indicate a lower temperature threshold at which the cable may suffer physical damage, degradation, and / or reduced operational capabilities or characteristics.

[0105] In some embodiments, cable analysis system 100 may automatically trigger an alert based on first data indicator 806 and / or second data indicator 808 reaching or exceeding warning threshold 804. For example, an alert may be triggered based on any one exceedance of warning threshold 804, based on the number of times data indicator 806 or 808 falls below or meets warning threshold 804, or based on the amount of time data indicator 806 or 808 has been below warning threshold 804. In some embodiments, graphical representation 802 generated using reporting module 800 may be adjusted to dynamically change warning threshold 804 and / or one or more of data indicators 806, 808 to further analyze conditions to which the cable system is exposed.

[0106] In some embodiments, previously generated graphical representations 802 (e.g., generated over a similar time period) may be overlaid to compare current asset or cable conditions and evaluate patterns of conditions that may require various corrective efforts by a work group. In some embodiments, a machine learning algorithm may be trained to dynamically adjust alert thresholds 804 to evaluate how specific conditions, such as temperature, pressure, wear, or damage, affect cable system types and cable system models in various areas. The machine learning algorithm may output alerts based on predicted corrective actions, inspection tasks, or planned maintenance. In some embodiments, the machine learning algorithm may evaluate the age of a monitored cable system, the amount of wear and tension that has occurred over a period of time under specific environmental conditions, and determine that maintenance is likely to be required within a specified time period. The time period may be dynamically adjusted based on the workload and continuously changing conditions to which the cable system is exposed.

[0107] 8B illustrates another exemplary dashboard interface that may be associated with the reporting module 800. Here, the reporting module 800 is shown associated with a dashboard interface including a representation or list of retired assets 809, according to aspects of the present disclosure. In analyzing cable systems and the various cables contained within them, it is often necessary to retire assets to ensure safe and effective operation of the assets. Asset retirement often involves data sets that can be tracked and analyzed to determine patterns and correlations between the type of cable used, the cable system's installation location, exposure conditions, and the installed cable system. Through analysis of these correlations and patterns, the lifespan of the cable system and its cables can be extended, or preventative measures can be taken based on a set of predictions to determine when asset replacement and subsequent retirement should occur.

[0108] In some embodiments, the reporting module 800 may include a list of retired assets 809 that is output as a report detailing the history of retired assets managed by the cable analysis system. The retired assets 809 may include an asset identifier, asset location, type of cable within the cable system, reason for retirement, total asset life before retirement, asset retirement approval, and retirement date.

[0109] The data contained in these reports is provided as input to machine learning algorithms that can be trained based on historical data, including operating conditions and the type of cable or cable system operated, to predict effective operational lifespans. The machine learning algorithms are continuously trained based on location-based data, the types of alerts received, and the reasons for retirement of similar assets, to predict and identify periods when assets should be considered for retirement and subsequent replacement.

[0110] Based on the prediction, the cable analysis system 100 may generate alerts to a work group or global dashboard to trigger corrective action toward retiring and replacing the cable, sensor system, or cable system. The prediction may also trigger automatic task assignment by the cable analysis system to a work group, prompting work group members to assign additional tasks toward extending the asset life through maintenance or troubleshooting, or preparing the asset for retirement or replacement.

[0111] FIG. 9 illustrates a work group interface 900 of a cable analysis system 100 according to an embodiment of the present disclosure. The work group interface 900 can provide an output (e.g., to a work group or a member of a work group) showing a set of assigned assets 902 corresponding to the work group. The work group interface 900 can provide a user with the cable system status 506, past inspections 504, and first and second performance charts 508a and 508b for the assigned assets 902 in the work group. In some embodiments, members of the work group can navigate among the various assets assigned to the work group to perform assessments of the various assets. Thus, the cable analysis system can automatically suggest priorities based on asset location, required corrective actions, or asset condition. As new tasks are added to a work group, either manually or automatically by the cable analysis system 100, the set of assigned assets 902 is dynamically updated with data related to those assets. Subsequently, as assets are removed or decommissioned, the set of assigned assets 902 may be updated to reflect the changes in the removed assets.

[0112] 10 is a diagram illustrating a task interface 1000 of a cable analysis system 100 according to an aspect of the present disclosure. The task interface 1000 can provide an analysis of tasks that have been automatically assigned by the cable analysis system 100 and / or manually assigned by an administrative user of the cable analysis system 100. The task interface can output a variety of different task groups, including groups for available tasks 1002, delayed tasks 1004, and completed tasks 1006.

[0113] Each active task 1002, delayed task 1004, and completed task 1006 may include all corresponding tasks (e.g., in either an active, delayed, or completed state) assigned to an individual member or work group. Each task group may provide an analysis of tasks currently being inspected, calibrated, in need of repair, and scheduled for installation. Each task group may further output a report on the status of each task included in the respective task group. For example, a task status report may be provided through a task repository 1008. The task repository 1008 may indicate the current status of a task, including the asset identifier associated with the task, the type of task to be completed, the specific type of task, the task status, the deadline for completing the task, and the work group or member to which the task is assigned. In some embodiments, the cable analysis system 100 may automatically update the task groups based on the current status or predicted completion date of each task currently assigned to each cable system being monitored. As the task status is updated for each work group, the tasks in each task group may be updated to active task 1002, delayed task 1004, or completed task 1006.

[0114] 11 illustrates an example method 1100 of a cable system analysis service for analyzing, inspecting, and monitoring a cable system including multiple cables. Although the illustrated method 1100 depicts a specific order of operations, the order may be changed without departing from the scope of the present disclosure. For example, some of the illustrated operations may be performed in parallel or in a different order without substantially affecting the functionality of the method 1100. In other embodiments, different components of an example device or system implementing the method 1100 may perform functions substantially simultaneously or in a specific order.

[0115] According to some embodiments, at block 1102, the method 1100 includes acquiring sensor data from a plurality of sensors, the sensor data including a plurality of sensor measurements associated with the cable. For example, the sensor module 108 shown in FIG. 1 may acquire the sensor data including a plurality of sensor measurements associated with the cable from a plurality of sensors (sensor systems 110a-110b of FIG. 1). In some embodiments, one or more of the plurality of sensor measurements are acquired using a non-contact sensor coupled to the cable or associated with the cable's environment. In some embodiments, at least a portion of the sensor data is acquired as sensor measurements intermittently transmitted by an intermittently reporting sensor included in the plurality of sensors.

[0116] Further, the method 1100 may include obtaining real-time sensor data including a plurality of real-time sensor measurements associated with the cable from a plurality of sensors. For example, the internal computing system 112 shown in FIG. 1 may obtain real-time sensor data including a plurality of real-time sensor measurements associated with the cable from a plurality of sensors of the sensor module 108 of FIG.

[0117] Further, method 1100 may include receiving a user selection of a particular cable from the plurality of cables represented in database 104 of FIG. 1. For example, internal computing system 112 of FIG. 1 may receive a user selection of a particular cable from the plurality of cables represented in database 104. In some embodiments, stored sensor readings of each selected type are obtained based on correlating each user input with a corresponding group identifier used in the database. In some embodiments, sensor readings of one or more selected types are determined based on one or more user inputs at a user interface.

[0118] Additionally, method 1100 may include determining a plurality of available sensor reading types corresponding to the stored sensor readings associated with the particular cable. For example, internal computing system 112 shown in FIG. 1 may determine a plurality of available sensor reading types corresponding to the stored sensor readings associated with the particular cable.

[0119] Further, method 1100 may include obtaining one or more selected types of sensor readings as selected by a user from a plurality of available sensor reading types for a particular cable. For example, user interface service 122 shown in FIG. 1 may obtain one or more selected types of sensor readings as selected by a user from a plurality of available sensor reading types for a particular cable.

[0120] Further, method 1100 may include configuring the intermittent reporting sensor with one or more predetermined reporting thresholds. For example, indication service 120 shown in FIG. 1 may configure the intermittent reporting sensor with one or more predetermined reporting thresholds. In some embodiments, the intermittent reporting sensor remains in a low power mode and does not report when collected sensor data is below at least one of the predetermined reporting thresholds. In some embodiments, the intermittent reporting sensor exits the low power mode and reports when collected sensor data exceeds at least one of the predetermined reporting thresholds.

[0121] According to some embodiments, at block 1104, method 1100 includes storing the sensor data in a database. For example, internal computing system 112 shown in Figure 1 may store the sensor data in database 104. In some embodiments, the sensor coupled to the cable includes a fiber optic sensor.

[0122] In some embodiments, the first subset of each sensor measurement is obtained from a sensor associated with the cable. In some embodiments, at least one sensor measurement in the first subset is obtained using a sensor coupled to the cable. In some embodiments, the battery-powered sensor includes a transceiver in communication with a receiver associated with a database.

[0123] In some embodiments, at least one sensor measurement of the first subset or the second subset of sensor data is obtained using a battery-powered sensor. In some embodiments, at least one sensor measurement of the second subset is obtained from a sensor associated with an asset located in the cable's environment. In some embodiments, the second subset of sensor data includes environmental kinematic measurements or baseline motion information determined for the cable's environment.

[0124] In some embodiments, each sensor measurement of the plurality of sensor measurements is associated with a group identifier included in a set of group identifiers. In some embodiments, the first subset of sensor data includes sensor measurements representative of physical properties of the cable. In some embodiments, the second subset of sensor data includes sensor measurements corresponding to an ambient environment of the cable.

[0125] Further, method 1100 may include performing motion compensation on a first subset of sensor data associated with the physical characteristics of the cable. For example, the analytics service 116 shown in FIG. 1 may perform motion compensation on the first subset of sensor data associated with the physical characteristics of the cable. In some embodiments, the motion compensation is performed based on environmental kinematic measurements or baseline motion information associated with the cable's surroundings. In some embodiments, performing motion compensation includes generating a refined version of the first subset of sensor data that removes the baseline motion information associated with the cable's surroundings.

[0126] According to some embodiments, at block 1106, the method 1100 includes, for each selected type of sensor measurements of the one or more selected types, obtaining a set of time-series sensor measurements of the selected type. For example, the analytics service 116 shown in FIG. 1 may obtain a set of time-series sensor measurements of the selected type for each selected type of sensor measurements of the one or more selected types. In some embodiments, an automatically generated work group is determined based on identifying one or more sensor measurements that correlate with a given cable. In some embodiments, the automatically generated work group includes one or more selected type sensor measurements. In some embodiments, each stored sensor measurement is associated with a different timestamp. In some embodiments, the one or more selected type sensor measurements are obtained based on an automatically generated work group of associated cables and sensors. In some embodiments, the one or more selected type sensor measurements are associated with the cable or with a work group that includes the cable and one or more physical assets different from the cable.

[0127] In some examples, work groups may be generated based on common characteristics among the cables in a cable system. Common characteristics may include characteristics such as physical properties, materials, or manufacturer. Creating work groups may enable a user to analyze the broader behavior of the cables and associated cable systems or to use artificial intelligence (AI) to train machine learning models to identify more global patterns.

[0128] According to some embodiments, at block 1108, the method 1100 includes determining, based on the set associated with the cable, one or more warning events associated with the cable. For example, the analytics service 116 shown in FIG. 1 may determine, based on the set of time-series sensor measurements, one or more warning events associated with the cable.

[0129] Additionally, the method 1100 may include determining one or more baseline variations associated with one or more of the sets of time-series sensor measurements. For example, the analytics service 116 shown in FIG. 1 may determine one or more baseline variations associated with one or more of the sets of time-series sensor measurements.

[0130] Further, method 1100 may include analyzing a recent portion of the set of time-series sensor measurements against the determined one or more baseline variations and one or more thresholds. For example, the analysis service 116 shown in FIG. 1 may analyze a recent portion of the set of time-series sensor measurements against the determined one or more baseline variations and one or more thresholds. In some embodiments, the one or more thresholds include predetermined thresholds determined based on one or more user inputs. In some embodiments, the one or more thresholds are determined based on an analysis of one or more sets of time-series sensor measurements corresponding to a ground truth labeled dataset.

[0131] Further, the method 1100 may include determining one or more ongoing warning events associated with the cable based on the set of time-series sensor measurements and the real-time sensor data. For example, the analytics service 116 shown in FIG. 1 may determine one or more ongoing warning events associated with the cable based on the set of time-series sensor measurements and the real-time sensor data.

[0132] For example, the internal computing system 112 shown in Figure 1 may generate a ground truth labeled data set based on obtaining historical results data associated with one or more reference cables. For example, the communication service 118 shown in Figure 1 may obtain historical results data associated with one or more reference cables.

[0133] Additionally, the internal computing system 112 shown in Figure 1 may generate the ground truth labeled data set based on obtaining reference measurements associated with one or more reference cables. For example, the communication service 118 shown in Figure 1 may obtain reference measurements associated with one or more reference cables.

[0134] Additionally, the internal computing system 112 shown in Figure 1 may generate the ground truth labeled dataset based on generating one or more ground truth labels based on correlating past results from the past outcome data with one or more features of the reference measurements. For example, the internal computing system 112 shown in Figure 1 may generate one or more ground truth labels based on correlating past results from the past outcome data with one or more features of the reference measurements.

[0135] Further, method 1100 may include identifying one or more deviations or anomalies in the analysis of the most recent portion of the set of time-series sensor measurements and generating an alert event based thereon. For example, internal computing system 112 shown in FIG. 1 may identify one or more deviations or anomalies in the analysis of the most recent portion of the set of time-series sensor measurements and generate an alert event based thereon.

[0136] According to some embodiments, at block 1110, the method 1100 includes automatically generating one or more corrective actions to correct the warning event at block 410. For example, the analytics service 116 shown in FIG. 1 may generate the one or more corrective actions in response to determining a warning event associated with the cable.

[0137] 12 illustrates an exemplary computer system 1200 for implementing aspects of the present disclosure. For example, the exemplary computer system 1200 may execute a client application for performing aspects of the present disclosure.

[0138] The exemplary computer system 1200 includes a processor 1205, memory 1210, a graphical device 1215, a network device 1220, an interface 1225, and a storage device 1230, all operatively connected via a bus 1235. The processor 1205 reads machine instructions (e.g., reduced instruction set (RISC), complex instruction set (CISC), etc.) loaded into the memory 1210 via a bootstrap process and executes an operating system (OS) for executing applications within a framework provided by the OS. For example, the processor 1205 may execute applications provided by a graphical framework such as Winforms, Windows Presentation Foundation (WPF), or Windows User Interface (WinUI), or a cross-platform user interface such as Xamarin or QT. In another example, the processor 1205 may execute applications written for a sandbox environment, such as a web browser.

[0139] Processor 1205 controls memory 1210 to store instructions, user data, OS content, and other content that cannot be stored internally to processor 1205 (e.g., in various caches). Processor 1205 may also control a graphical device 1215 (e.g., a graphical processor) that outputs graphical content to a display 1240. In some embodiments, graphical device 1215 may be integrated into processor 1205. In yet other embodiments, display 1240 may be integrated into computer system 1200 (e.g., a laptop, tablet, phone, etc.).

[0140] The graphical device 1215 may be optimized to perform floating-point operations, such as graphical operations, or may be configured to perform other operations on behalf of the processor 1205. For example, the graphical device 1215 may be controlled by instructions for performing numerical operations optimized for floating-point operations. For example, the processor 1205 may assign instructions to the graphical device 1215 to perform processing optimized for the graphical device 1215. For example, the graphical device 1215 may perform processing related to artificial intelligence (AI), natural language processing (NLP), or vector operations. The results may be returned to the processor 1205. In another example, an application running on the processor 1205 may issue instructions to the processor 1205 requesting that the graphical device 1215 perform processing. In another example, the graphical device 1215 may return processing results to another computer system (i.e., distributed computing).

[0141] The processor 1205 may also control a network device 1220 that transmits and receives data using multiple wireless channels 1245 and at least one communication standard (e.g., Wi-Fi (802.11ax, 802.11e, etc.), Bluetooth, various standards provided by the Third Generation Partnership Project (e.g., 3G, 4G, 5G, etc.), or a satellite communication network (e.g., Starlink)). The network device 1220 may connect wirelessly to a network 1250 and connect to a server 1255 or other service provider. The network device 1220 may be connected to the network 1250 via a physical (i.e., wired) connection. The network device 1220 may connect directly to a local electronic device 1260 using a point-to-point (P2P) connection or a short-range wireless connection.

[0142] The processor 1205 may also control an interface 1225 that connects to an external device 1270 for bidirectional or unidirectional communication. The interface 1225 may be implemented by any suitable interface that forms a line connection, such as a universal serial bus (USB), Thunderbolt, or the like. The external device 1265 may receive data from the interface 1225 and process the data or perform functions for different applications running on the processor 1205. For example, the external device 1265 may be another display device, a musical instrument, a computer interface device (e.g., a keyboard, a mouse, etc.), an audio device (e.g., an analog-to-digital converter (ADC), a digital-to-analog converter (DAC)), a storage device for storing content, an authentication device, an external network interface (e.g., a 5G hotspot), a printer, etc.

[0143] Figure 13 illustrates an example of a system for implementing certain aspects of the present technology. In particular, Figure 13 illustrates an example of a computing system 1300, which may be, for example, an internal computing system, a remote computing system, a camera, or any computing device comprising its components, the components of which communicate with each other using connections 1305. The connections 1305 may be physical connections via a bus or direct connections to a processor 1310 in a chipset architecture. The connections 1305 may be virtual connections, network connections, or logical connections.

[0144] In some embodiments, computing system 1300 is a distributed system, and the functionality described in this disclosure may be distributed across a data center, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents multiple similar components, each performing some or all of the functionality described for that component. In some embodiments, these components may be physical or virtual devices.

[0145] The exemplary system 1300 includes at least one processing unit (CPU or processor) 1310 and connections 1305 coupling various system components, including system memory 1315 (such as read-only memory (ROM) 1320 and random access memory (RAM) 1325), to the processor 1310. The computing system 1300 may also include a cache of high-speed memory 1312 directly connected to, located nearby, or integrated as part of the processor 1310.

[0146] Processor 1310 may include any general-purpose processor and hardware or software services, such as services 1332, 1334, 1336 stored in memory 1330, that are configured to control processor 1310. It may also include special-purpose processors where software instructions are embedded in the actual processor design. Processor 1310 may be a completely self-contained computing system, essentially with multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0147] To enable user interaction, computing system 1300 includes input device(s) 1345, which may include any number of input mechanisms, such as a microphone for audio, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, or voice input. Computing system 1300 may also include output device(s) 1335, which may be one or more of many output mechanisms known to those skilled in the art. In some cases, a multimodal system may enable a user to provide multiple types of input and output for communication with computing system 1300. Computing system 1300 may also include a communications interface 1340, which may generally coordinate and manage user input and system output. There is no limitation to operation with any particular hardware configuration, and therefore the basic features herein may be easily substituted for improved hardware or firmware configurations as they are developed.

[0148] The computing system 1300 may also include a communications interface 1340, which may generally manage and manage user input and system output. The communications interface may perform or facilitate the receipt and / or transmission of wired and / or wireless communications using wired and / or wireless transceivers.Specific examples include audio jacks / plugs, microphone jacks / plugs, Universal Serial Bus (USB) ports / plugs, Apple™ Lightning™ ports / plugs, Ethernet ports / plugs, Fiber Optic ports / plugs, proprietary wired ports / plugs, wireless signal transmission over 3G, 4G, 5G and / or other cellular data networks, Bluetooth™ wireless signal transmission, Bluetooth™ Low Energy (BLE) wireless signal transmission, iBeacon® wireless signal transmission, Radio-Frequency Identification (RFID) wireless signal transmission, Near-Field Communication (NFC) wireless signal transmission, Dedicated Short Range Communication (DSRC) wireless signal transmission, 802.11 Wi-Fi wireless signal transmission, Wireless Local Area Network (WLAN) signal transmission, Visible Light Communication (VLC), Worldwide Interoperability Microwave Access (WiMAX), and other wireless technologies. Interoperability for Microwave Access, Infrared (IR) radio signal transmission, Public Switched Telephone Network (PSTN) signal transmission, Integrated Services Digital Network (ISDN) signal transmission, Ad-hoc Network signal transmission, radio wave signal transmission, microwave signal transmission, infrared signal transmission, visible light signal transmission, ultraviolet signal transmission, radio signal transmission along the electromagnetic spectrum, or some combination of these.The communications interface 1340 may include one or more Global Navigation Satellite System (GNSS) receivers or one or more GNSS transceivers used to determine the location of the computing system 600 based on receiving one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the United States' Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo GNSS. There is no restriction on operating with a particular hardware configuration, and therefore the basic features herein can be easily substituted for improved hardware or firmware configurations as they are developed.

[0149] The storage device 1330 is a non-volatile and / or non-transitory and / or computer-readable memory device, such as a hard disk or other type of computer-readable medium that can store data that can be accessed by a computer. Examples of storage devices 1330 include magnetic cassettes, flash memory cards, solid-state memory devices, digital versatile disks, cartridges, floppy disks, flexible disks, hard disks, magnetic tapes, magnetic strips / stripe, any other magnetic storage media, flash memory, memorized memory, other solid-state memories, compact disc read only memories (CD-ROMs), rewritable compact discs (CD-RWs), digital video optical discs (DVDs), Blu-ray discs (BDs), holographic optical discs, other optical media, secure digital cards (SD cards), micro secure digital cards (microSD cards), memory sticks (registered trademark) cards, smart card chips, EMV chips, subscriber identity module (SIM cards), mini / micro / nano / pico SIM cards, other integrated circuit (IC) chips / cards, random access memory (RAM), static RAM (SRAM), RAM), Dynamic RAM (DRAM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)Programmable Read-Only Memory), Flash EPROM, cache memory (e.g., Level 1 cache (L1), Level 2 cache (L2), Level 3 cache (L3), Level 4 cache (L4), Level 5 cache (L5), other (L#) cache), Resistive Random-Access Memory (RRAM / ReRAM), Phase Change Memory (PCM), Spin Transfer Torque RAM (STT-RAM), other memory chips or cartridges, and / or combinations thereof.

[0150] Storage 1330 may include software services, servers, other services, etc., where code defining such software, when executed by processor 1310, causes the system to perform functions. In some embodiments, hardware services that perform a particular function may include software components stored on computer-readable media associated with the hardware components necessary to perform that function (e.g., processor 1310, connections 1305, output devices 1335, etc.). "Computer-readable media" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, retaining, or holding instructions and / or data. Computer-readable media may also include non-transitory media capable of storing data, which excludes carrier waves and / or transitory electronic signals propagating over wireless or wired connections. Examples of non-transitory media include, but are not limited to, magnetic disks or tapes, optical storage media such as compact discs (CDs) or digital versatile discs (DVDs), flash memory, memory, or memory devices. A computer-readable medium may store code and / or machine-executable instructions that represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means, such as memory sharing, message passing, token passing, network transmission, etc.

[0151] While the foregoing description provides specific details to provide a thorough understanding of the embodiments and examples, those skilled in the art will recognize that the present application is not limited thereto. Accordingly, while exemplary embodiments of the present application have been described in detail, those skilled in the art should understand that the concepts of the present invention can be embodied and utilized in various ways, and the appended claims should be construed to include such variations except insofar as limited by the prior art. The various features and aspects of the present application described above can be used individually or in any combination. Furthermore, the embodiments can be utilized in numerous environments and applications other than those described herein without departing from the broad spirit and scope of the present application. Accordingly, the specification and drawings should be regarded in an illustrative rather than a restrictive sense. For illustrative purposes, methods have been described in a particular order. It should be understood that in alternative embodiments, methods may be performed in an order different from that described.

[0152] For clarity of explanation, in some cases, the present technology may be presented as including individual functional blocks, which may include devices, device components, method steps or routines embodied in software, or functional blocks comprising a combination of hardware and software. Additional components other than those shown and / or described herein may be used. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form so as not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail so as to avoid obscuring the embodiments.

[0153] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0154] Particular embodiments may be described as a process or a method that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. While a flowchart may depict operations as a sequential process, many operations may be performed in parallel or simultaneously. Moreover, the order of operations may be rearranged. A process terminates when the operation is completed, but may have additional steps not included in the diagram. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or to the main function.

[0155] The processes and methods according to the above-described examples can be implemented using computer-executable instructions stored on or available from a computer-readable medium. Such instructions can include, for example, instructions and data that cause or configure a general-purpose computer, a special-purpose computer, or a processing device to perform a certain function or group of functions. Some of the computer resources used can be accessible over a network. The computer-executable instructions can be, for example, binaries, intermediate-format instructions (e.g., assembly language), firmware, or source code. Examples of computer-readable media that can be used to store instructions, information used, and / or information generated during the execution of the methods according to the described examples include magnetic or optical disks, flash memory, USB devices with non-volatile memory, network-attached storage devices, etc.

[0156] In some embodiments, computer-readable storage devices, media, memories may include cabled or wireless signals containing bitstreams, etc. However, when referred to, non-transitory computer-readable storage media expressly excludes media such as energy, carrier signals, electromagnetic waves, and signals in the proper sense.

[0157] Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, as the case may be, depending in part on the particular application, desired design, corresponding technology, etc.

[0158] The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and may take various forms. When implemented in software, firmware, middleware, or microcode, program code or code segments that perform the necessary tasks (e.g., a computer program product) may be stored on a computer-readable or machine-readable medium. A processor may perform the necessary tasks. Examples of forms include laptops, smartphones, mobile phones, tablet devices, and other small form factor personal computers, personal digital assistants, rack-mounted devices, stand-alone devices, etc. The functionality described herein may also be incorporated into a peripheral device or add-in card. Such functionality may also be implemented on a circuit board surrounded by various chips or processes executing on a single device, as a further example.

[0159] The instructions, media for carrying such instructions, computational resources for executing them, and other structures supporting the computational resources are exemplary means for providing the functionality described in this disclosure.

[0160] The techniques described herein may be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in a variety of devices, such as a general-purpose computer, a wireless communication device handset, or a versatile integrated circuit device, including applications in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as interoperable discrete logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium bearing program code including instructions that, when executed, perform one or more of the methods, algorithms, and / or processes described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may include memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, etc. Additionally or alternatively, the technology may be implemented at least in part by a computer-readable communications medium, such as a propagated signal or wave, that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer.

[0161] The program code may be executed by one or more processors, which may include one or more digital signal processors (DSPs), general-purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such processors may be configured to execute any of the techniques described in this disclosure. The general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in combination with a DSP core, or other such configurations. Thus, the term "processor" as used herein may refer to any of the foregoing components, any combination of the foregoing components, or other components or devices suitable for implementing the techniques described herein.

[0162] One of ordinary skill in the art will understand that the symbols or terms "less than (<XX)" and "greater than (>XX)" used herein may be replaced with the symbols "less than or equal to (≦)" and "greater than or equal to (≧)", respectively, without departing from the scope of this specification.

[0163] When a component is described as being "configured to perform" a particular operation, such a configuration can be achieved by, for example, designing electronic circuitry or other hardware to perform the operation, programming a programmable electronic circuit (e.g., a microprocessor or other suitable electronic circuit) to perform the operation, or a combination thereof.

[0164] The term "coupled to" refers to a component that is directly or indirectly physically connected to another component and / or that is in direct or indirect communication with the other component (e.g., a component that is connected to the other component via a wired or wireless connection and / or other suitable communication interface).

[0165] Claim language or other phrases containing the phrases "at least one of" and / or "one or more than one" mean that one element of the set, or multiple elements of the set (in any combination), satisfies the claim. For example, claim language such as "at least one of A and B" or "at least one of A or B" means A, B, or A and B. As another example, claim language such as "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, A and C, B and C, or A, B, and C. The phrases "at least one of" and / or "one or more than one" do not limit the set to the listed items. For example, claim language such as "at least one of A and B" or "at least one of A or B" means A, B, or A and B, and can further include items not recited in the set A and B.

[0166] Aspects of the present disclosure include the following.

[0167] Aspect 1: A method comprising: acquiring sensor data from a plurality of sensors, the sensor data comprising a plurality of sensor measurements associated with a synthetic fiber cable; storing the sensor data in a database with each sensor measurement of the plurality of sensor measurements associated with a group identifier included in a group identifier set; acquiring, for each selected type of one or more selected types of sensor measurements, a set of time-series sensor measurements of the selected types from the stored sensor measurements associated with different timestamps; determining one or more warning events associated with the synthetic fiber cable based on the set of time-series sensor measurements; and automatically generating one or more corrective actions to correct the warning events.

[0168] Aspect 2: The method of aspect 1, wherein determining one or more of the warning events associated with the synthetic fiber cable includes determining one or more baseline fluctuations associated with one or more of the time series sensor measurement sets; analyzing a recent portion of the time series sensor measurement sets against the determined one or more baseline fluctuations and one or more thresholds; and identifying one or more deviations or anomalies in the analysis of the recent portion of the time series sensor measurement sets and generating a warning event based thereon.

[0169] Aspect 3: The method of aspect 2, wherein the one or more thresholds include a predetermined threshold determined based on one or more user inputs, or the one or more thresholds are determined based on an analysis of one or more time-series sensor measurement sets corresponding to a ground truth labeled dataset.

[0170] Aspect 4: The method of aspect 3, further including generating a ground truth labeled dataset based on obtaining past result data associated with one or more reference cables, obtaining reference measurements associated with the one or more reference cables, and generating one or more ground truth labels based on correlating past results of the past result data with one or more features of the reference measurements.

[0171] Aspect 5: A method according to any one of aspects 1 to 4, comprising: acquiring real-time sensor data from the plurality of sensors, the real-time sensor data including a plurality of real-time sensor measurements associated with the synthetic fiber cable; determining one or more ongoing warning events associated with the synthetic fiber cable based on the time-series sensor measurement set and the real-time sensor data; and automatically generating one or more alerts indicating the one or more ongoing warning events associated with the synthetic fiber cable.

[0172] Aspect 6: A method according to any one of aspects 1 to 5, wherein the one or more selected types of sensor measurements are obtained based on receiving a user selection for a particular cable from a plurality of cables represented in the database, determining a plurality of available sensor measurement types corresponding to sensor measurements stored in association with the particular cable, and obtaining the one or more selected types of sensor measurements for the particular cable as selected by the user from the plurality of available sensor measurement types.

[0173] Aspect 7: A method described in any of aspects 1 to 6, wherein the one or more selected types of sensor measurements are determined based on one or more user inputs in a user interface, and the set of stored sensor measurements of each selected type is obtained based on correlating each user input with a corresponding group identifier used in the database.

[0174] Aspect 8: A method according to any of aspects 1 to 7, wherein the one or more selected types of sensor measurements are obtained based on automatically generated work groups of associated cables and sensors, the automatically generated work groups including the one or more selected types of sensor measurements, and the automatically generated work groups are determined based on identifying one or more types of sensor measurements that correlate with a given cable.

[0175] Aspect 9: A method according to any of aspects 1 to 8, wherein the one or more selected types of sensor measurements are associated with the synthetic fiber cable or associated with a work group that includes the synthetic fiber cable and one or more physical assets different from the synthetic fiber cable.

[0176] Aspect 10: A method according to any one of aspects 1 to 9, wherein the sensor data includes a first subset of sensor data including sensor measurements representing physical properties of the synthetic fiber cable, the first subset of sensor measurements being obtained from sensors associated with the synthetic fiber cable, and a second subset of sensor data including sensor measurements corresponding to an ambient environment of the synthetic fiber cable.

[0177] Aspect 11: The method of any of Aspects 1-10, wherein at least one sensor measurement of the first subset is obtained using a sensor coupled to the synthetic fiber cable.

[0178] Aspect 12: The method of any one of Aspects 1 to 11, wherein the sensor coupled to the synthetic fiber cable includes an optical fiber sensor.

[0179] Aspect 13: A method according to any of aspects 1 to 12, wherein at least one sensor measurement of the first and second subsets of sensor data is obtained using a battery-powered sensor, the battery-powered sensor including a transceiver unit that communicates with a receiver unit associated with the database.

[0180] Aspect 14: A method according to any of aspects 1 to 13, wherein at least one sensor measurement of the second subset is obtained from a sensor associated with an asset located in the surrounding environment of the synthetic fiber cable.

[0181] Aspect 15: A method according to any of aspects 1 to 14, wherein the second subset of sensor data includes environmental kinematic measurements or baseline motion information determined for the surrounding environment of the synthetic fiber cable.

[0182] Aspect 16: A method according to any one of aspects 1 to 15, further comprising performing motion compensation on the first subset of sensor data associated with physical properties of the synthetic fiber cable, the motion compensation being performed based on the environmental kinematic measurements or the baseline motion information associated with the surrounding environment of the synthetic fiber cable.

[0183] Aspect 17: A method according to any of aspects 1 to 16, wherein performing the motion compensation includes generating a refined version of the first subset of sensor data that removes the baseline motion information associated with the surrounding environment of the synthetic fiber cable.

[0184] Embodiment 18: A method according to any one of embodiments 1 to 17, wherein one or more of the plurality of sensor measurements are obtained using a non-contact sensor coupled to the synthetic fiber cable or associated with the surrounding environment of the synthetic fiber cable.

[0185] Aspect 19: The method according to any one of aspects 1 to 18, wherein at least a portion of the sensor data is acquired as sensor measurement values ​​intermittently transmitted by an intermittently reporting sensor included in the plurality of sensors.

[0186] Aspect 20: A method according to any one of aspects 1 to 19, further comprising configuring the intermittent reporting sensor with one or more predetermined reporting thresholds, wherein the intermittent reporting sensor remains in a low power mode and does not report when collected sensor data is below at least one of the predetermined reporting thresholds, and the intermittent reporting sensor exits the low power mode and reports when collected sensor data exceeds at least one of the predetermined reporting thresholds.

[0187] For clarity of explanation, in some cases, the present technology may be presented as including individual functional blocks, which may include devices, device components, method steps or routines embodied in software, or functional blocks comprising a combination of hardware and software.

[0188] Any step, operation, function, or process described herein may be performed or implemented alone with hardware and software services or combinations of services, or in combination with other devices. In some embodiments, a service is software residing in memory of a client device and / or one or more servers of a content management system, and may perform one or more functions when a processor executes the software associated with the service. In some embodiments, a service is a program or collection of programs that perform a particular function. In some embodiments, a service may be considered a server. Memory may be a non-transitory computer-readable medium.

[0189] In some embodiments, computer-readable storage devices, media, memories may include cabled or wireless signals containing bitstreams, etc. However, when referred to, non-transitory computer-readable storage media expressly excludes media such as energy, carrier signals, electromagnetic waves, and signals in the proper sense.

[0190] The methods according to the above examples can be implemented using computer-executable instructions stored on or available from a computer-readable medium. Such instructions can include, for example, instructions and data that cause or configure a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a certain function or group of functions. Some of the computer resources used can be accessible over a network. The computer-executable instructions can be, for example, binaries, intermediate-format instructions (e.g., assembly language), firmware, or source code. Examples of computer-readable media that can be used to store instructions, information used, and / or information generated during the execution of the methods according to the described examples include magnetic or optical disks, solid-state memory devices, flash memory, USB devices with non-volatile memory, network-attached storage devices, etc.

[0191] A device implementing a method according to the present disclosure may comprise hardware, firmware, and / or software and may adopt any of a variety of form factors. Typical examples of such form factors include servers, laptops, smartphones, small form factor personal computers, personal digital assistants, etc. The functionality described herein may also be incorporated into a peripheral device or add-in card. Such functionality may also be implemented on a circuit board surrounded by various chips or processes executing on a single device, as a further example.

[0192] The instructions, media that carry the instructions, computer resources for executing the instructions, and other structures that support the computer resources are means for providing the functionality described in this disclosure.

[0193] Although various examples and other information have been used to describe aspects within the scope of the appended claims, this should not be intended to limit the claims based on any particular features or arrangements in such examples, as one skilled in the art would be able to derive a wide variety of implementations using these examples. Furthermore, while some subject matter may be described in terms specific to example structural features and / or method steps, it should be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functions may be distributed differently or performed by different components than those identified herein. Rather, the described functions and steps are disclosed as example components of systems and methods within the scope of the appended claims.

Claims

1. acquiring sensor data from a plurality of sensors, the sensor data including a plurality of sensor measurements related to the synthetic fiber cable; storing the sensor data in a database, each sensor measurement of the plurality of sensor measurements being associated with a group identifier included in a set of group identifiers; For each selected type of sensor measurement among the one or more selected types, obtaining a set of time series sensor measurements of the selected type from the stored sensor measurements associated with different timestamps; determining one or more warning events associated with the synthetic fiber cable based on the set of time series sensor measurements; and automatically generating one or more corrective actions to correct the warning event.

2. 10. The method of claim 1, wherein determining one or more warning events associated with the synthetic fiber cable comprises: determining one or more baseline variations associated with one or more of the sets of time-series sensor measurements; analyzing a recent portion of the set of time series sensor measurements against the determined one or more baseline variations and one or more thresholds; and identifying one or more deviations or anomalies in analyzing the most recent portion of the set of time-series sensor measurements and generating an alert event based thereon.

3. 3. The method of claim 2, the one or more thresholds include a predetermined threshold determined based on one or more user inputs; or The one or more thresholds are determined based on an analysis of one or more sets of time-series sensor measurements corresponding to a ground truth labeled dataset.

4. 4. The method of claim 3, obtaining historical result data associated with one or more reference cables; obtaining reference measurements associated with the one or more reference cables; generating one or more ground truth labels based on correlating historical outcomes of the historical outcome data with one or more features of the reference measurements; generating a ground truth labeled dataset.

5. 10. The method of claim 1, acquiring real-time sensor data from the plurality of sensors, the real-time sensor data including a plurality of real-time sensor measurements associated with the synthetic fiber cable; determining one or more ongoing warning events associated with the synthetic fiber cable based on the set of time series sensor measurements and the real-time sensor data; and automatically generating one or more alerts indicating the one or more ongoing warning events associated with the synthetic fiber cable.

6. 2. The method of claim 1, wherein the one or more selected types of sensor measurements are: receiving a user selection of a particular cable from among a plurality of cables represented in the database; determining a plurality of available sensor measurement types corresponding to stored sensor measurements associated with the particular cable; and obtaining the one or more selected types of sensor measurements for the particular cable as selected by a user from the plurality of available sensor measurement types.

7. 10. The method of claim 1, the one or more selected types of sensor measurements are determined based on one or more user inputs at a user interface; The set of stored sensor measurements of each selected type is obtained based on correlating each user input with a corresponding group identifier used in the database.

8. 10. The method of claim 1, the one or more selected types of sensor measurements are obtained based on an automatically generated work group of associated cables and sensors, the automatically generated work group including the one or more selected types of sensor measurements; The automatically generated work groups are determined based on identifying one or more sensor measurements that correlate with a given cable.

9. 2. The method of claim 1, wherein the one or more selected types of sensor measurements are associated with the synthetic fiber cable or with a work group that includes the synthetic fiber cable and one or more physical assets different from the synthetic fiber cable.

10. 2. The method of claim 1, wherein the sensor data comprises: a first subset of sensor data including sensor measurements representative of physical properties of the synthetic fiber cable, the first subset of sensor measurements being obtained from sensors associated with the synthetic fiber cable; and a second subset of sensor data including sensor measurements corresponding to an ambient environment of the synthetic fiber cable.

11. 11. The method of claim 10, wherein the first subset of at least one sensor measurement is obtained using a sensor coupled to the synthetic fiber cable.

12. 12. The method of claim 11, wherein the sensor coupled to the synthetic fiber cable comprises a fiber optic sensor.

13. 11. The method of claim 10, wherein at least one sensor measurement of the first and second subsets of sensor data is obtained using a battery-powered sensor, the battery-powered sensor including a transceiver that communicates with a receiver associated with the database.

14. The method of claim 10 , wherein at least one sensor measurement of the second subset is obtained from a sensor associated with an asset located in the surrounding environment of the synthetic fiber cable.

15. 11. The method of claim 10, wherein the second subset of sensor data includes environmental kinematic measurements or baseline motion information determined for the surrounding environment of the synthetic fiber cable.

16. 16. The method of claim 15, performing motion compensation on the first subset of sensor data associated with a physical characteristic of the synthetic fiber cable; The motion compensation is based on the environmental kinematic measurements or the baseline motion information associated with the surrounding environment of the synthetic fiber cable.

17. 17. The method of claim 16, wherein performing the motion compensation includes generating a refined version of the first subset of sensor data that removes the baseline motion information associated with the surrounding environment of the synthetic fiber cable.

18. 10. The method of claim 1, wherein one or more of the plurality of sensor measurements are obtained using a non-contact sensor coupled to the synthetic fiber cable or associated with an environment surrounding the synthetic fiber cable.

19. 10. The method of claim 1, wherein at least a portion of the sensor data is obtained as sensor measurements transmitted intermittently by intermittently reporting sensors included in a plurality of sensors.

20. 20. The method of claim 19, further comprising configuring the intermittent reporting sensor with one or more predetermined reporting thresholds; the intermittently reporting sensor is maintained in a low power mode and does not report when collected sensor data is below at least one of the predetermined reporting thresholds; The intermittently reporting sensor exits the low power mode and reports when collected sensor data exceeds at least one of the predetermined reporting thresholds.

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