Apparatus, method and computer program product for monitoring remaining useful life of an asset

Abstract

The invention is entitled "Apparatus, method, and computer program product for monitoring remaining useful life of an asset." Embodiments of the present disclosure provide asset life monitoring. One or more models can be utilized to generate estimated values of factors representing root causes of operational anomalies of an asset. An estimated remaining life value of one or more root cause variables and / or an estimated second remaining life value of an asset health index representing a combination of the one or more root cause variables can be generated, the estimated remaining life value indicating a time until a value of a root cause variable is estimated to reach a particular limit threshold corresponding to the root cause variable. The second remaining life value of the asset health index can be provided to enable processing of the second remaining life value as a remaining useful life of the asset based on the overall root cause variables. The first remaining life values of one or more individual root cause variables can be provided to enable more detailed insight into individual root causes of operational degradation of the asset.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to monitoring the remaining useful life of one or more assets of a system, and in particular, to generating and providing the remaining useful life of one or more assets based on system data corresponding to any number of root cause variables associated with the one or more target assets. BACKGROUND

[0002] Assets (e.g., subsystems or individual assets) of an operating system are typically maintained on a regular schedule in an attempt to keep the useful life of the assets as long as possible without incurring downtime due to degradation in the operation of the assets. Applicant has discovered problems with current implementations relating to monitoring the useful life of assets. Through exertions of skill, ingenuity and innovation, Applicant has solved many of these recognized problems by developing solutions embodied in the present disclosure, which will be described in detail below. SUMMARY

[0003] Generally, embodiments of the present disclosure provided herein provide improvements in monitoring the remaining useful life of an asset. Other implementations for monitoring the remaining useful life of an asset will be or become apparent to one with skill in the art after reviewing the following drawings and detailed description. All such additional implementations shall be considered within the scope of the present disclosure and are protected by the following claims.

[0004] According to a first aspect of the present disclosure, a method is provided. The method can be computer-implemented via one or more computing devices embodied in hardware, software, firmware, and / or combinations thereof as described herein. An example implementation of the method is performed at a device having one or more processors and one or more memories. The example method includes receiving system data associated with an operating system comprising a target asset. The example method also includes determining at least one operational anomaly associated with the operating system from the received system data. The example method also includes identifying at least one root cause variable associated with the at least one operational anomaly. The example method also includes generating a first remaining life value associated with the at least one root cause variable, the first remaining life value corresponding to the target asset. The example method also includes generating a second remaining life value associated with an asset health index. The example method also includes providing the second remaining life value.

[0005] Additionally or alternatively, in some example implementations of the method, the asset health index represents a combination of each of the at least one root cause variable.

[0006] Additionally or alternatively, in some example embodiments of the method, the first remaining life value is generated utilizing a model. Additionally or alternatively, in some such example embodiments of the method, the second remaining life value is generated utilizing the model. Additionally or alternatively, in some such example embodiments of the method, the model determines that a first root cause variable of the at least one root cause variable and a second root cause variable of the at least one root cause variable have a direct relationship that influences the second remaining life value.

[0007] Additionally or alternatively, in some example embodiments of the method, the example method further includes presenting at least one deviation between an expected trend of the at least one root cause variable and an actual trend of the at least one root cause variable.

[0008] Additionally or alternatively, in some example embodiments of the method, at least a portion of the received system data includes data from at least one sensor associated with the target asset.

[0009] Additionally or alternatively, in some example embodiments of the method, at least a portion of the system data is associated with an upstream asset or a downstream asset associated with the target asset.

[0010] Additionally or alternatively, in some example embodiments of the method, the example method further includes determining that the target asset is utilizing a selected mode of a plurality of configurable modes, determining the second remaining life variable based on the selected mode.

[0011] Additionally or alternatively, in some example embodiments of the method, receiving the system data includes receiving at least a portion of the system data in real-time at a set interval.

[0012] Additionally or alternatively, in some example embodiments of the method, the example method further includes initiating a maintenance notification based on the second remaining life value.

[0013] Additionally or alternatively, in some example embodiments of the method, providing the second remaining life value includes presenting the second remaining life value, the method further including receiving user input requesting display of the first remaining life value of the at least one root cause variable; and presenting the first remaining life value.

[0014] According to another aspect of the present disclosure, an example system is provided. In at least one example embodiment, the example system includes at least one processor and at least one memory. The at least one memory has computer program code stored thereon, which, when executed by the at least one processor, configures the system to perform any of the example methods described herein. In yet another example embodiment, the example system includes means for performing each step of any of the example methods described herein.

[0015] According to yet another aspect of the present disclosure, an example computer program product is provided. The example computer program product includes at least one non-transitory computer-readable storage medium having computer program code stored thereon, the computer program code, when executed by at least one processor, configures the at least one processor to perform any of the example methods described herein. BRIEF DESCRIPTION OF DRAWINGS

[0016] Accordingly, having generally described the embodiments of the present disclosure, reference will now be made to the drawings, which are not necessarily drawn to scale, and wherein:

[0017] Figure 1 A block diagram of a system that can be specially configured for monitoring one or more assets within which embodiments of the present disclosure can operate is shown;

[0018] Figure 2 A block diagram of an example life monitoring device that can be specially configured according to example embodiments of the present disclosure is shown;

[0019] Figure 3A A visualization of an example computing environment for generating a remaining life value using a model according to at least some example embodiments of the present disclosure is shown;

[0020] Figure 3B A visualization of an example computing environment for generating a root cause limit threshold for a root cause variable according to at least some example embodiments of the present disclosure is shown;

[0021] Figure 3C A visualization of an example computing environment for generating a remaining life value for a root cause variable according to at least some example embodiments of the present disclosure is shown;

[0022] Figure 4 An example user interface providing a remaining life value associated with an asset health index for an asset or system according to at least some example embodiments of the present disclosure is shown;

[0023] Figure 5Another example user interface for providing a remaining life value associated with a particular root cause variable of an asset or system is shown, according to at least some example embodiments of the present disclosure;

[0024] Figure 6 A flow chart illustrating operational blocks including an exemplary process for monitoring the remaining useful life of an asset, according to at least some exemplary embodiments of the present disclosure;

[0025] Figure 7 A flow chart illustrating additional operational blocks including an exemplary process for generating at least one remaining useful life value based on a selected mode when monitoring remaining useful life values ​​of an asset, according to at least some exemplary embodiments of the present disclosure;

[0026] Figure 8 a flowchart illustrating additional operational blocks including an exemplary process for providing a trend deviation of at least one root cause variable when monitoring an asset remaining useful life value, in accordance with at least some exemplary embodiments of the present disclosure; and

[0027] Figure 9 A flow chart is shown including additional operational blocks of an exemplary process for providing a remaining useful life value for at least one root cause variable when monitoring an asset's remaining useful life value, in accordance with at least some exemplary embodiments of the present disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the present disclosure are shown. Indeed, embodiments of the present disclosure can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Throughout, like reference numerals refer to like elements.

[0029] Overview

[0030] For any of a variety of reasons, system engineers, owners, and operators (collectively, "users") often attempt to track the operational performance of a particular operating system and / or its assets. Among these reasons, such users often attempt to track the operational performance of a particular asset (or assets) to determine when maintenance of such assets is appropriate. For example, while an asset is operating, changes in the asset's operation and / or one or more conditions associated with its operation may cause the asset's performance to begin to deteriorate, with such degradation continuing until the asset deteriorates to the point where it can no longer be used for its intended purpose (e.g., the asset has no remaining useful life). Maintenance of the one or more assets may be performed to extend the asset's lifespan and / or otherwise ensure that the asset's operational condition remains within a desired level. For example, a user may seek to ensure that certain operational conditions corresponding to specific root cause variables that may cause an asset to cease functioning as intended, thereby resulting in downtime, remain within target thresholds. By monitoring such root cause variables, a user can initiate maintenance on the asset early enough so that the asset does not break, malfunction, or otherwise cease to perform as intended. An asset that ceases to perform as intended may result in downtime for the individual asset, or in worse cases, for the operation of the entire system or plant. It is desirable to maintain consistent uptime for all assets, and ideally to maintain the highest possible uptime.

[0031] Typically, data is collected about the operation of a particular asset, and the data can be analyzed by a user to determine whether the asset will remain operational based solely on the data value. In this regard, it is often relied upon for the user to determine how long the asset will remain available based on a particular data variable (e.g., a single root cause variable). The inventors have identified a number of problems and inefficiencies associated with conventional implementations for monitoring asset life. For example, manual analysis of such data is often incorrect, resulting in assets having a useful life that is significantly shorter or longer than the determined life. In the event that the remaining useful life of a particular asset is overestimated, the asset may fail, resulting in downtime for the asset, system, and / or plant, additional costs associated with fully repairing the asset, and / or indirect problems resulting from the failure (e.g., loss of production). Alternatively, if a user estimates that the remaining useful life of an asset is significantly shorter than it actually is, maintenance may be performed on the asset event even though maintenance is not yet required, resulting in additional unnecessary downtime for maintenance and unnecessary expenditure of additional resources (e.g., additional costs and additional man-hours) to implement the maintenance.

[0032] In an attempt to avoid the drawbacks of conventional methods for determining the remaining useful life of a particular asset, maintenance is typically performed on the particular asset at regular intervals. However, such routine maintenance is subject to similar and additional problems and inefficiencies. For example, where routine maintenance is performed on an asset with significant remaining useful life, such maintenance may result in unnecessary costs, unnecessary downtime, etc. For example, where maintenance on a particular asset is expensive or difficult (e.g., because the asset is difficult to access or is particularly critical with respect to the operation of a particular system or plant), such unnecessary maintenance may be particularly expensive and / or time-consuming, resulting in, for example, particularly detrimental downtime of the entire operating system of the asset, system, and / or plant.

[0033] Embodiments of the present disclosure provide for monitoring the operational health associated with a particular asset. The operational health associated with the particular asset can be determined based on system information obtained from the asset and / or one or more sensors associated with the asset. In this regard, upon receiving each batch of system data associated with each asset, each batch of system data can be processed to monitor the operational health of the asset based on the values ​​of one or more factors represented in or derivable from such system data. For example, the system data can include or be used to calculate the values ​​of one or more root cause variables associated with the particular asset.

[0034] The operational health of a particular asset may be affected by one or more particular factors that represent the root causes of an operational anomaly and / or asset failure. In this regard, the value of each factor that represents a root cause variable may be determined based on received system data associated with the particular asset. Additionally or alternatively, the root cause variables may each individually or in combination affect the remaining useful life of the overall asset. For example, the remaining useful life may represent the length of time that the asset may continue to operate without maintenance before the asset is expected to no longer operate as expected and / or is expected to fail due to one or a combination of root cause variables that affect the performance of the asset. The remaining useful life of the asset may be affected by each factor individually (e.g., each factor may be a unique root cause of a failure or other anomaly) or by a combination of factors (e.g., a combination of factors that represent root cause variables result in a failure or other anomaly).

[0035] In some embodiments of the present disclosure, future values ​​of a particular factor (such as a root cause variable associated with a target asset) are predicted over future time intervals. Embodiments of the present disclosure may generate an estimated value for a particular or each particular individual root cause variable. Additionally or alternatively, specific system data (e.g., received sensor data associated with a target asset) and / or derivatives thereof (such as one or more estimated future values ​​of a particular root cause variable) may be used to generate a remaining life value and an associated limit threshold associated with the particular root cause variable. The remaining life value represents the length of time until the value of the root cause variable is determined or otherwise estimated to violate the limit threshold corresponding to the root cause variable, indicating that a failure or other operational anomaly may occur due, at least in part, to the root cause variable. Such embodiments provide an estimate indicating when the value of a particular root cause variable that a user is interested in monitoring is likely to cause a failure or other anomaly in the operation of a particular target asset. In this regard, the remaining life value may be provided to enable maintenance of a particular aspect of the target asset to be performed before the root cause variable causes asset downtime.

[0036] Additionally or alternatively, at least some embodiments of the present disclosure generate an estimate of an asset health index that represents the overall operational health of the asset. The asset health index may represent the overall operational health of the target asset based at least on a combination of one or more root cause variables. In this regard, system data and / or derivatives thereof (such as an estimate of the asset health index) may similarly be used to generate a second remaining useful life value associated with the asset health index. The second remaining useful life value may be based on the estimate of the asset health index and an associated limit threshold for the asset health index. The second remaining useful life value represents the remaining useful life of the overall target asset, e.g., the remaining time until a combination of one or more root cause variables affecting the asset may result in downtime due to a failure or other operational anomaly of the target asset. Such embodiments provide an estimate indicating when the value of the asset health index indicates that the target asset may experience an operational failure or other anomaly due to degradation of a combination of one or more root cause variables. In this regard, a second remaining useful life value reflecting the remaining useful life of the asset may be provided to facilitate faster identification of when a particular asset may fail or experience other operational anomaly, without requiring individual manual analysis of each root cause variable.

[0037] The remaining useful life of a particular target asset, as represented by an asset health index, can provide insights into the overall operational health of one or more assets, along with individual insights into the impact of each root cause variable, based on the corresponding remaining useful life values ​​of such root cause variables. For example, an asset health index can be based on any number of root cause variables that represent factors that influence the remaining useful life of an asset. In this regard, each root cause variable can be associated with a life remaining value that indicates a time interval until the particular root cause variable will reach and / or exceed a particular predefined threshold (e.g., a threshold that, if reached, indicates that the asset may fail due to the root cause variable). A second life remaining value can be generated that represents the remaining useful life of the asset (or multiple assets) based on the individual and / or combined totals of the root cause variables. Thus, the remaining useful life represented by the second remaining useful life value provides a more accurate representation of how long one or more assets can continue to operate as expected without maintenance, based on all such factors. Utilizing the generated remaining useful life, maintenance of the asset can be more accurately performed to ensure that the asset does not experience a failure. Similarly, unnecessary maintenance can be reduced to increase overall asset and system uptime, while additionally or alternatively saving resources (e.g., manpower, monetary resources, computing resources, etc.) that would otherwise be wasted on unnecessary maintenance performed based on conventional implementations for monitoring asset health.

[0038] An exemplary context includes an individual asset or system subject to pressure, temperature, and load. System data associated with an asset or system may be collected for use in processing associated with the asset or system. System data may include real-time sensor data associated with the asset or system, either continuously or at specific time intervals (e.g., every 1 minute, 5 minutes, 30 minutes, hourly, etc.). Sensor data may be collected from sensors within the asset or system, located in the environment of the asset or system, etc. System data may also include data manually entered or otherwise collected based on the operation of the individual asset or system.

[0039] The system data may be processable to determine a value for each of the root cause variables of pressure, temperature, and load based on the system data. Additionally or alternatively, the system data may be processed to generate remaining life values ​​for some or all of the individual root cause variables of pressure, temperature, and / or load, for example, using a model. Such remaining life values ​​for the individual root cause variables may be generated to represent the time remaining until each individual root cause variable reaches a corresponding limit threshold, indicating that the value of the root cause variable has reached an unacceptable level (e.g., potentially resulting in an asset not operating as expected, a complete failure, downtime, etc.).

[0040] Based on the values ​​of individual root cause variables of pressure, temperature and / or load and / or life remaining values ​​associated with such root cause variables, a second remaining life value reflecting the remaining useful life of the individual asset or system can be generated. A second life remaining value reflecting the remaining useful life of the asset or system can be generated based on a combination of root cause variables, for example using a model, the second life remaining value indicating the length of time remaining until the asset or system may fail. In this regard, a user or a computing system can utilize the remaining useful life reflected by the second remaining life value to accurately determine when to initiate maintenance on the asset or system to minimize or prevent downtime, without requiring the user to derive such determination from individual root cause variables. Alternatively or additionally, an implementation scheme may provide notifications associated with maintenance at specific times (e.g., at predetermined intervals before determining that the remaining useful life reaches zero). Such technical improvements and advantages are not provided by conventional implementation schemes for monitoring assets of an operating system.

[0041] definition

[0042] In some embodiments, some of the operations described above may be modified or further amplified. In addition, in some embodiments, additional optional operations may also be included. Modifications, amplifications, or additions to the operations described above may be performed in any order and in any combination.

[0043] Those skilled in the art to which the present disclosure pertains will appreciate many modifications and other embodiments of the present disclosure as set forth herein, having benefited from the teachings presented in the foregoing description and the associated drawings. It will be appreciated, therefore, that the embodiments are not limited to the specific embodiments disclosed, and that modifications and other embodiments are intended to be included within the scope of the appended claims. In addition, although the foregoing description and the associated drawings have described exemplary embodiments in the context of certain exemplary combinations of elements and / or functions, it will be understood that different combinations of elements and / or functions may be provided by alternative embodiments without departing from the scope of the appended claims. In this regard, for example, it is also conceivable to combine elements and / or functions that are different from those explicitly described above, as may be shown in some of the appended claims. Although specific terms are employed herein, they are used only in a general and descriptive sense, and not for the purpose of limitation.

[0044] The term "asset" refers to a computing device, mechanical device, or system. An asset may include any number of sub-assets.

[0045] The term "target asset" refers to an asset embodying a computing or mechanical device or system, wherein data associated with the asset is to be processed to determine the operational health of the computing or mechanical device or system.

[0046] The term "upstream asset" refers to a second asset of a system whose operation affects the operation of a particular target asset. In this regard, the operational health of an upstream asset relative to a particular target asset may affect the operational health of the target asset.

[0047] The term "downstream asset" refers to a second asset of a system whose operation is affected by the operation of a particular target asset. In this regard, the operational health of the target asset can affect the operational health of the downstream asset.

[0048] The term "mode" refers to a configurable state of a particular asset or system that is used to cause the particular asset or system to operate in a particularly corresponding manner. The term "selected mode" refers to a data value representing the current utilization mode of the particular asset or system. Non-limiting examples of modes include manual mode, automatic mode, and mixed mode, where the mode the asset or system is currently configured to utilize represents the selected mode. It should be understood that some assets and / or systems do not utilize configurable modes, and other assets and / or systems utilize any number of configurable modes.

[0049] The term "operating system" refers to one or more assets configured to operate in conjunction with one another. Non-limiting examples of an operating system include an industrial power plant or a manufacturing facility that includes any number of monitored devices and / or systems to perform the functions of the facility.

[0050] The term "system data" refers to manually entered data, automatically derived data, and / or sensor data from and / or associated with a particular asset or system, associated with interactions between assets, associated with the environment of an asset, and / or associated with the operation of an asset. System data may include any number of discrete data components from different inputs.

[0051] The term "operational anomaly" refers to an indication of asset degradation or asset performance degradation based on system data associated with the asset.

[0052] The term "variable" refers to electronically managed data that represents a measured or calculated data signal.

[0053] The term "root cause variable" refers to an electronic data representation of any variable whose value is affected by the performance of a particular target asset or at least one asset that affects the performance of the target asset. A particular target asset may be associated with any number of root cause variables that reflect measurable and / or determinable factors associated with the operational health of the asset. In some embodiments, the value of a root cause variable may be derived from system data associated with the particular asset. An asset may be manually associated with any number of root cause variables.

[0054] The term "asset health index" refers to an electronic data representation of the overall operational health of an asset based on a combination of one or more root cause variables. The value of the asset health index represents the overall operational health of the asset based on one or more root cause variables associated with the asset and / or a combination of root cause variables associated with the asset.

[0055] The term "remaining useful life" associated with an asset refers to the estimated time interval during which the subject asset will continue to operate within acceptable parameters without maintenance, based on one or more root cause variables.

[0056] The term "limit threshold" refers to a cutoff value for a particular root cause variable or combination of root cause variables that, if violated, indicates that an asset may experience a failure or operational anomaly due to the root cause variable or combination of root cause variables.

[0057] The term "remaining life value" refers to an electronic management data value representing a predicted time interval until the value of a root cause variable reaches a threshold value. In this regard, for a particular target asset associated with one or more root cause variables, a different remaining life value may be determined for each root cause variable and for a particular threshold value corresponding to the particular root cause variable.

[0058] The term "second remaining useful life value" refers to an electronic management data value representing a predicted time interval representing the remaining useful life of a target asset based on any number of root cause variables. In some embodiments, the second remaining useful life value represents the time at which an asset health index associated with a combination of root cause variables for a particular target asset reaches a limit threshold corresponding to the asset health index.

[0059] The term "model" refers to a statistical, algorithmic, and / or machine learning algorithm or set of algorithms that generates a remaining life value for an asset based on one root cause variable or a combination of each of multiple root cause variables.

[0060] The term "direct relationship" with respect to one or more root cause variables refers to a relationship between data values ​​that indicates that the remaining useful life of the asset consistently decreases or consistently increases as the value of a first root cause or combination of root cause variables increases.

[0061] The term "expected trend" refers to the estimated difference in values ​​between two or more points in time.

[0062] The term "actual trend" refers to an identified, non-estimated difference in value between two or more points in time based on received data.

[0063] The term "sensor" refers to a physical computing device embodied in hardware, software, and / or firmware that measures one or more specific values ​​associated with the operation of one or more assets.

[0064] The term "maintenance notification" refers to data indicating that a maintenance action should be performed on at least one asset of a system.

[0065] Exemplary systems of the present disclosure

[0066] Figure 1 A block diagram of a system that may be specifically configured for monitoring one or more assets, within which embodiments of the present disclosure may operate, is shown. Specifically, Figure 1 The life monitoring system 102 is depicted in communication with an operating system 150. In some embodiments, the monitoring system 102 is configured to communicate with each of the depicted computing devices (e.g., various assets, systems, and / or associated sensors) directly or indirectly through direct communication with another device (e.g., a controller). In other embodiments, for example, as depicted, the monitoring system 102 is configured to communicate with one or more computing devices via a communication network 116.

[0067] Communication network 116 may embody any of a variety of networks configured to facilitate communication between two or more computing devices. In some embodiments, communication network 116 embodies a private network. For example, monitoring system 102 may be embodied by various computing devices on an internal network, such as one or more servers of an industrial plant that communicate with various controllers, assets, and / or sensors associated with operating the industrial plant. In some such embodiments, monitoring system 102 may be embodied by computing devices located near the industrial plant and / or other computing devices to be monitored (e.g., within the same plant site or other physically defined location).

[0068] In other embodiments, communication network 116 embodies a public network, such as the Internet. In some such embodiments, monitoring system 102 may embody a remote or "cloud" system that accesses a computing device of operating system 150 via communication network 116 from a location separate from the physical location of operating system 150. For example, monitoring system 102 may be embodied by computing devices at a central headquarters, a server farm, a distributed platform, etc. In some such embodiments, monitoring system 102 may be accessed directly (e.g., via a display and / or peripheral devices operably coupled to monitoring system 102) and / or may be accessed indirectly through the use of a client device. For example, in some embodiments, a user may log in (e.g., using a username and password) or otherwise access monitoring system 102 to access the described functionality relative to one or more specific operating systems, plant locations, etc. Alternatively or additionally, in some embodiments, monitoring system 102 is specifically associated with operating system 150 to provide access to the described functionality specifically relative to operating system 150, e.g., without requiring additional user authentication.

[0069] Monitoring system 102 includes one or more computing devices embodied in hardware, software, firmware, etc., that provide the asset life monitoring functionality contemplated herein (e.g., monitoring the operational health of one or more assets, determining anomalies associated with one or more aspects of an asset, determining one or more remaining life values ​​for one or more root cause variables or combinations of root cause variables, etc.). As depicted, monitoring system includes server 102A and data repository 102B, which may each be embodied by one or more computing devices that can communicate with each other to provide the functionality described herein.

[0070] Server 102A may include one or more computing devices embodied in hardware, software, firmware, etc., configured to ingest and / or process data to provide the described functionality. In some embodiments, server 102A receives system data from one or more assets operating system 150 or otherwise associated with these assets. For example, in some embodiments, server 102A communicates via communication network 116 to receive or otherwise collect system data via sensors associated with each asset. Alternatively or additionally, in some embodiments, server 102A communicates directly with one or more assets and / or associated controllers via communication network 116 to receive system data reflecting configurations and / or other data values ​​associated with data characteristics of the assets. Still additionally or alternatively, in some embodiments, server 102A is configured to process system data to provide various functionality. For example, in some embodiments, server 102A processes system data associated with a particular asset to determine, identify, and / or otherwise detect at least one anomaly in the operation of the asset based on the system data. Additionally or alternatively, in some embodiments, server 102A is configured to generate one or more remaining life values, e.g., associated with a root cause variable and / or a combination of root cause variables associated with at least one such anomaly. In some such embodiments, server 102A is configured to generate one or more of such remaining life values ​​using a model specifically configured to process system data to generate a value for each of the root cause variables associated with at least one anomaly. In this regard, server 102A can monitor the operational health of one or more assets by generating a remaining life value that reflects at least the remaining useful life of each of the one or more assets, and in some embodiments, generating a remaining life value associated with the root cause variables that contribute to the remaining useful life of the assets.

[0071] The data repository 102B may include one or more computing devices embodied in hardware, software, firmware, etc., configured to store and / or otherwise maintain data associated with asset life monitoring functionality. In some embodiments, the data repository 102B stores system data associated with one or more assets of one or more operating systems. Additionally or alternatively, in some embodiments, the data repository 102B stores values ​​of one or more root cause variables associated with a particular asset and / or corresponding metadata (e.g., timestamp information indicating when the values ​​were collected or generated, etc.). Still alternatively or additionally, in some embodiments, the data repository 102B stores derived data associated with a particular asset, such as a value of a health index associated with the particular asset, a remaining useful life indicating a second remaining useful life value of the asset based on one or more root cause variables, etc. Alternatively or additionally, in some embodiments, the data repository 102B is configured to store remaining useful life values ​​(e.g., indicating the remaining useful life of the asset) and / or associated metadata (e.g., timestamp information indicating when the values ​​were collected or generated, etc.) associated with an asset, a particular root cause variable, and / or a combination of root cause variables.

[0072] In some embodiments, for example, data repository 102B may be embodied by one or more database servers, memory devices, etc. configured to store such system data. In some embodiments, data repository 102B includes one or more remote or "cloud" databases accessible via one or more networks, such as communication network 116 or a separate communication network (e.g., the Internet).

[0073] Operating system 150 includes multiple subsystems and assets that provide specific functions (e.g., functions associated with the operation of a specific industrial plant). As shown, operating system 150 includes assets 104A, 106A, 108A, and 110A. Operating system 150 also includes various sensors associated with various assets, including sensors that are separate from or otherwise external to individual assets, such as sensor 104D and sensor 110D. In some embodiments, the various sensors further embody assets for which monitoring system 102 collects and / or processes data. Operating system 150 also includes system 114, which embodies subsystems of operating system 150 and includes multiple associated computing devices that operate in conjunction to perform specific functions. In some embodiments, subsystem 114 further embodies assets for which monitoring system 102 collects and / or processes data. Operating system 150 also includes controllers 112A and 112B that can communicate with various other assets of operating system 150. In some embodiments, controller 112A and / or controller 112B each embodies an asset for which data is collected and / or processed by monitoring system 102 .

[0074] Operating system 150 includes one or more controllers, specifically controller 112A and controller 112B. Each controller may be embodied by one or more computing devices embodied in hardware, software, firmware, etc., which provide activation and / or other input signals to each asset. Non-limiting examples of such controllers include programmable logic controllers, proportional controllers, differential controllers, etc. Each controller may be configured to activate and / or otherwise initiate operation of one or more assets that may communicate with the controller or otherwise be configured to be controlled by the controller. For example, as shown, controller 112A controls each of assets 104A and 106A and external sensor 104D associated with asset 104A. Controller 112B controls each of the assets of subsystem 114 (specifically assets 108A and 110A) along with external sensor 110D corresponding to asset 110A.

[0075] Each of assets 104A, 106A, 108A, and 110A may embody various components that operate to provide specific functionality. In an exemplary context, each of the assets embodies a computing device used to operate one or more systems of a residential building (e.g., an HVAC asset, a security asset, etc.). In another exemplary context, each of the assets embodies a computing device used to operate one or more systems of a manufacturing plant (e.g., a manufacturing machine, a conveyor belt, etc.). System 114 may include multiple sub-assets as part of a larger subsystem that operate and / or are controlled together, are housed together, or otherwise operate in conjunction with one another.

[0076] Each of assets 104A, 106A, 108A, and 110A may include one or more actuators 104C, 106C, 108C, and 110C. Actuators 104C, 106C, 108C, and 110C may each be activated to operate each of the corresponding assets. For example, actuator 104C may be activated and / or otherwise manipulated to operate asset 104. Similarly, actuator 108C may be activated and / or otherwise manipulated to operate asset 108. In this regard, each of assets 104A, 106A, 108A, and 110A may be activated via a corresponding actuator to complete one or more operations performed by the asset.

[0077] Each of assets 104A, 106A, 108A, and 110A may optionally include or otherwise be associated with one or more internal sensors, such as embodied by sensors 104B, 106B, 108B, and / or 110B. Each of sensors 104B, 106B, 108B, and / or 110B may monitor one or more aspects of the operational health of the corresponding asset 104A, 104A, 106A, or 110A, respectively. For example, such sensors may include temperature sensors, pressure sensors, gas sensors, and the like. Each of the sensors for a particular asset may monitor the value of one or more specific data characteristics that embody root cause variables associated with the operation of the corresponding asset. Data values ​​from the sensors may be continuously collected at set or predefined time intervals (e.g., every minute, every 5 minutes, every hour, etc.). In this regard, the sensors may be configured to collect values ​​of the root cause variables associated with the corresponding asset in real time at one or more desired times.

[0078] One or more assets may optionally be associated with one or more sensors external to the asset itself. For example, as depicted, sensor 110D is associated with asset 110A such that sensor 110D monitors one or more aspects of the operational health of asset 110A. Similarly, as depicted, sensor 104D is associated with asset 104A such that sensor 104D monitors one or more aspects of the operational health of asset 104A. The external sensors may each monitor one or more aspects of the corresponding asset itself, or the environment surrounding or otherwise associated with the asset. For example, sensor 104D may monitor the temperature environment of asset 104A, or may monitor the concentration of toxic gases in the environment of asset 104A. In this regard, such external sensors may provide system data embodying such values ​​of root cause variables corresponding to the data characteristics monitored by the sensors. The system data embodying such values ​​may be provided to the monitoring system 102 for storage and / or further processing, as described herein.

[0079] In some embodiments, one or more assets are associated with at least one upstream asset or downstream asset. For example, a particular asset may be associated with at least one upstream asset whose operational health affects the particular asset. Alternatively or additionally, a particular asset may be associated with at least one downstream asset such that the operational health of the particular asset affects the operational health of the downstream asset. It should be understood that particular assets may be marked by one or more data values ​​indicating a correlation between the assets, such as whether the assets are upstream assets or downstream assets of each other. In some embodiments, one or more assets of a system are upstream and / or downstream with respect to each other, such as where the assets of the system operate in a particularly defined order to provide a particular function. In some embodiments, upstream assets control and / or otherwise activate downstream assets.

[0080] As illustrated, for example, asset 110A represents a downstream asset relative to asset 108A. Similarly, asset 108A represents an upstream asset relative to asset 110A. In this regard, an operational defect or failure of asset 108A may impact the operational health of asset 110A. In this regard, for example, a root cause of at least one anomaly in the operation of asset 108A may similarly impact the operation of asset 110A, and / or an operational defect or failure of asset 108A may impact the operational health of asset 110A. For example, in some embodiments, asset 108A activates or otherwise controls asset 110A, such that an operational defect of asset 108A similarly causes control of asset 110A to become defective. In other embodiments, controller 112B controls asset 108A independently of asset 110A.

[0081] Exemplary devices of the present disclosure

[0082] The methods, apparatuses, systems, and computer program products of the present disclosure may be implemented by any of a variety of devices. For example, the methods, apparatuses, systems, and computer program products of the exemplary embodiments may be implemented by a fixed computing device such as a personal computer, a computing server, a computing workstation, or a combination thereof. Additionally, the exemplary embodiments may be implemented by any of a variety of mobile terminals, mobile phones, smartphones, laptop computers, tablet computers, or any combination thereof.

[0083] In at least one exemplary embodiment, the monitoring system 102 is comprised of one or more computing systems such as Figure 2 embodied in the life monitoring device 200 shown in the figure. As shown, the life monitoring device 200 includes a processor, a memory 204, an input / output circuit 206, a communication circuit 208, a data monitoring circuit 210, and a life detection circuit 212. Although the components are described with respect to functional limitations, it should be understood that a particular specific implementation necessarily includes the use of specific hardware. It should also be understood that in some embodiments, certain components described herein include similar or common hardware. For example, in some embodiments, two groups of circuits use the same processor, network interface, storage medium, etc. to perform their associated functions, so that each group of circuits does not require duplicate hardware. Therefore, it should be understood that the use of the term "circuit" as used herein with respect to the components of the life monitoring device 200 includes specific hardware configured to perform the functions associated with the specific circuit groups described herein.

[0084] Additionally or alternatively, the term“circuitry” should be understood in broad terms as including hardware and, in some embodiments, including software and / or firmware for configuring hardware. For example, in some embodiments,“circuitry” refers to and / or includes processing circuitry, storage media, network interfaces, input / output devices, etc. In some embodiments, other elements of the life monitoring device 200 provide or supplement the functionality of particular circuitry. For example, in some embodiments, the processor 202 provides processing functionality to one or more of the other circuitry groups, the memory 204 provides storage functionality, the communication circuitry 208 provides network interface functionality, etc.

[0085] In some embodiments, the processor 202 (and / or a co-processor or any other processing circuitry that assists or is otherwise associated with the processor) communicates with the memory 204 via a bus for passing information among the components of the device. The memory 204 is non-transitory and, in some embodiments, includes, for example, one or more volatile and / or non-volatile memories. In other words, in some embodiments, the memory contains non-transitory electronic memory devices (e.g., computer readable storage media). In some embodiments, the memory 204 is configured to store information, data, content, applications, instructions for enabling the life monitoring device 200 to perform various functions in accordance with exemplary embodiments of the present disclosure. In some embodiments, for example, the memory 204 contains one or more databases for storing user data objects, electronic data objects, and / or other data associated with data objects, and / or is otherwise configured to maintain such data objects for access and / or update, as described herein.

[0086] In various embodiments of the present disclosure, the processor 202 is embodied in any of a multitude of manners and can, for example, comprise one or more processing devices configured to independently execute. Additionally or alternatively, the processor 202 can comprise one or more processors in series via a bus to enable independent execution of instructions, pipelining, and / or multithreading. The use of the terms“processor,”“processing module,” or“processing circuitry” should be understood to encompass single core processors, multi-core processors, multiple processors on a single chip, other central processing units (“CPUs”), microprocessors, integrated circuits, and / or remote or“cloud” processors.

[0087] In an exemplary embodiment, the processor 202 is configured to execute computer-coded instructions stored in the memory 204 or otherwise accessible to the processor. Additionally or alternatively, in some embodiments, the processor 202 is configured to perform hard-coded functions. Thus, whether configured by hardware methods or software methods, or by a combination thereof, the processor 202 may represent an entity (e.g., physically embodied in the form of circuits) capable of performing operations according to embodiments of the present disclosure while being configured accordingly. Alternatively or additionally, in another exemplary case, when the processor is embodied as an executor of software instructions, the instructions specifically configure the processor 202 to perform the algorithms and / or operations described herein when executing the instructions.

[0088] As an exemplary context, processor 202 is configured to support asset life monitoring functionality. In some such embodiments, for example, processor 202 is configured to receive system data associated with an operating system including a target asset. Additionally or alternatively, in some embodiments, processor 202 is configured to determine at least one operational anomaly associated with the operating system based at least on the received system data. Additionally or alternatively, in some embodiments, processor 202 is configured to identify at least one root cause variable associated with the at least one operational anomaly. Additionally or alternatively, in some embodiments, processor 202 is configured to generate a first remaining life value associated with one or more of the at least one root cause variables, each of which is associated with the target asset. For example, processor 202 may access and / or otherwise utilize a model configured to generate the first remaining life value. Additionally or alternatively, in some embodiments, processor 202 is configured to generate a second remaining life value associated with the asset health index, for example, where the second remaining life value reflects a remaining useful life associated with the target asset. For example, in some embodiments, the processor 202 may access and / or otherwise utilize a model configured to generate a second remaining life value, which in some embodiments is similarly configured to generate a first remaining life value. Additionally or alternatively, in some embodiments, the processor 202 is configured to provide at least a second remaining life value, such as a second remaining life value based on at least the first remaining life value.

[0089] In some embodiments, lifespan monitoring device 200 includes input / output circuitry 206 that, either alone or in communication with processor 202, provides output to a user and / or receives indications of user input. In some embodiments, input / output circuitry 206 includes one or more user interfaces and / or includes a display to which a user interface can be presented. In some embodiments, input / output circuitry 206 includes a web user interface, a mobile application, a desktop application, a linked or networked client device, and the like. In some embodiments, input / output circuitry 206 also includes any of a plurality of peripheral devices, a keyboard, a mouse, a joystick, a touch screen, a touch area, soft keys, a microphone, a speaker, or other input / output mechanisms. In some such embodiments, the input / output mechanisms are configured to enable a user to provide data representing one or more user interactions for processing by lifespan monitoring device 200. A processor and / or input / output circuitry 206 that can communicate with a processor, such as processor 202, is configured to control one or more functions of one or more user interface elements via computer program instructions (e.g., software and / or firmware) stored in a memory accessible to the processor (e.g., memory 204, etc.).

[0090] In some embodiments, the life monitoring device 200 includes a data monitoring circuit 210. The communication circuit 208 is embodied as any device, such as a device or circuit embodied in hardware or a combination of hardware and software, which is configured to receive and / or transmit data from and / or to a network and / or any other device, circuit or other module that communicates with the life monitoring device 200. In this regard, in some embodiments, the communication circuit 208 includes at least a network interface, for example, for implementing communications with a wired or wireless communication network. For example, in some embodiments, the communication circuit 208 includes one or more network interface cards, antennas, buses, switches, routers, modems and supporting hardware and / or software, or any other device suitable for implementing communications via a network. Additionally or alternatively, the communication interface may include circuitry for interacting with one or more antennas to enable signals to be transmitted via the one or more antennas or to process signals received via the one or more antennas.

[0091] The data monitoring circuitry 210 includes hardware, software, firmware, and / or combinations thereof configured to support data collection and storage functions associated with the asset life monitoring functions of the life monitoring system 102. In some embodiments, the data monitoring circuitry 210 utilizes processing circuitry, such as the processor 202, to perform one or more of these actions. Additionally or alternatively, in some embodiments, the data monitoring circuitry 210 utilizes one or more portions of communication circuitry, such as some or all of the communication circuitry 208, to communicate with one or more other computing devices and / or receive data from such computing devices.

[0092] In some embodiments, the data monitoring circuit 210 includes hardware, software, firmware, and / or a combination thereof to receive system data. The data monitoring circuit 210 may receive system data from one or more sensors associated with one or more assets, controllers associated with one or more assets, directly from the assets, and the like. In some embodiments, the data monitoring circuit 210 includes hardware, software, firmware, and / or a combination thereof for requesting and / or receiving such system data, for example, in real time. Additionally or alternatively, in some embodiments, the data monitoring circuit 210 includes hardware, software, firmware, and / or a combination thereof for automatically receiving system data, for example, at specific time intervals (e.g., every minute, every 5 minutes, every hour, etc.) or continuously in real time. Additionally or alternatively, in some embodiments, the data monitoring circuit 210 includes hardware, software, firmware, and / or a combination thereof for storing and / or maintaining system data and / or data derived therefrom in one or more repositories. In some embodiments, for example, the data monitoring circuit 210 includes software, hardware, firmware, and / or a combination thereof for storing received system data in a first data repository for storage of such data maintained on or otherwise accessible by the life monitoring device 200, and / or storing a remaining life value associated with a particular asset in a second data repository for storage of such derived data maintained on or otherwise accessible by the life monitoring device 200. It should be understood that in some embodiments, the data monitoring circuit 210 includes or is embodied as a separate processor, a specially configured field programmable gate array (FPGA), and / or a specially configured application specific integrated circuit (ASIC).

[0093] Life monitoring circuitry 212 includes hardware, software, firmware, and / or combinations thereof configured to support operations associated with life monitoring system 102. In some embodiments, life monitoring circuitry 212 utilizes processing circuitry, such as processor 202, to perform one or more of these actions.

[0094] In some embodiments, the life monitoring circuitry 212 includes hardware, software, firmware, and / or a combination thereof for determining at least one anomaly associated with a target asset based on received system data associated with the target asset. In some such embodiments, the life monitoring circuitry 212 includes hardware, software, firmware, and / or a combination thereof for retrieving the received system data associated with one or more assets from a specific data repository or a data repository maintained or accessible by the life monitoring device 200. Additionally or alternatively, in some embodiments, the life monitoring circuitry 212 includes hardware, software, firmware, and / or a combination thereof for identifying at least one root cause variable associated with each of the at least one anomaly. Additionally or alternatively, in some embodiments, the life monitoring circuitry 212 includes hardware, software, firmware, and / or a combination thereof for generating a remaining life value for each of the at least one root cause variable, e.g., wherein each generated remaining life value represents a time to reach a specific threshold associated with the root cause variable. In some embodiments, the life monitoring circuitry 212 generates each remaining life value using a model specifically configured to generate such remaining life values.

[0095] Additionally or alternatively, in some embodiments, the lifespan monitoring circuitry 212 includes hardware, software, firmware, and / or a combination thereof for generating a second lifespan remaining value for an asset health index associated with the target asset, e.g., where the second lifespan remaining value represents the remaining useful life of the entire target asset. In some embodiments, the second lifespan remaining value is generated based on a combination of one or more root cause variables, e.g., based on the value of each of the combination of one or more root cause variables and / or based on the first lifespan remaining value for each root cause variable. In some embodiments, the lifespan monitoring circuitry utilizes a model to generate the second lifespan remaining value for the target asset, e.g., in some embodiments, the same model is configured to generate the first lifespan remaining value for each root cause variable.

[0096] Additionally or alternatively, in some embodiments, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for providing a second life remaining value associated with the target asset and / or one or more first life remaining values ​​associated with the target asset. In some embodiments, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for causing an interface associated with and / or otherwise including a remaining life value to be presented to provide such life values. Additionally or alternatively, in some embodiments, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for transmitting one or more specially configured transmissions including the remaining life value to provide such remaining life value (e.g., to a second device for further processing and / or display).

[0097] Additionally or alternatively, in some embodiments, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for determining a selected pattern associated with the target asset for generating the first remaining useful life and / or for generating the second remaining useful life associated with the target asset.

[0098] Additionally or alternatively, in some embodiments, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for further processing one or more of the first life remaining value and / or the second life remaining value associated with the target asset. In some embodiments, for example, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for initiating a maintenance notification based on the second remaining life value. Additionally or alternatively, in some embodiments, the life monitoring circuit 212 includes hardware, software, firmware, and / or a combination thereof for initiating one or more sub-processes (e.g., maintenance and / or repair processes) based on the first remaining life value, a combination of the first remaining life values, and / or the second life remaining value. It will be appreciated that, in some embodiments, the life monitoring circuit 212 includes or is embodied as a separate processor, a specially configured field programmable gate array (FPGA), and / or a specially configured application specific integrated circuit (ASIC).

[0099] In some embodiments, one or more of the foregoing groups of circuits are combined to form a single group of circuits. The single combined group of circuits can be configured to perform some or all of the functions described herein with respect to the individual groups of circuits. For example, in at least one embodiment, the data monitoring circuit 210 and the life monitoring circuit 212 are embodied by a single group of circuits, and / or one or more of the data monitoring circuit 210 and / or the life monitoring circuit 212 are combined with the processor 202. Additionally or alternatively, in some embodiments, one or more of the groups of circuits described herein are configured to perform one or more of the actions described with respect to one or more of the other groups of circuits.

[0100] Exemplary Data Visualizations of the Disclosure

[0101] Figure 3A A visualization of an exemplary computing environment for generating a remaining life value using a model is shown in accordance with at least some example embodiments of the present disclosure. In this regard, the exemplary computing environment and various data described in association therewith can be maintained by one or more computing devices, such as the life monitoring device 200. For example, the life monitoring device 300 can be specially configured via hardware, software, firmware, and / or a combination thereof to maintain each of the depicted data elements and process the data elements as depicted and described.

[0102] With respect to Figure 3A The illustrated computing environment is configured to generate one or more remaining life values from system data associated with at least a target asset of an operating system. In this regard, the computing environment can be configured to execute an exemplary process for providing asset life monitoring functionality. For example, a remaining life value for a separate root cause variable associated with one or more anomalies of a particular target asset can be generated, and / or a second remaining life value for an asset health index associated with a particular target asset can be generated. In this regard, the second remaining life value associated with the asset health index of a target asset provides insight into the overall remaining useful life of the target asset, and each first remaining life value for a root cause variable provides insight into a separate factor affecting the remaining useful life of the target asset in the event such further insight into the operational health of the target asset is desired.

[0103] As illustrated, the system data includes a plurality of sensor data 302A-302C (collectively, “system data 302”). The system data 302 can be received from any number of sensors and / or other computing devices associated with or otherwise embodying an asset for processing at any various times. For example, in some embodiments, the system data 302 is received in real-time over a particular timestamp interval. Values of such real-time data can be temporarily stored and / or processed over a particular timestamp interval (e.g., 1 minute, 5 minutes, etc.) to determine a processed value (e.g., an average value, a weighted average value, or other algorithmically determined value) for the particular data received over the particular timestamp interval. The processed value can be stored for further processing over a second timestamp interval, such as for processing over a longer period of time (e.g., hours, days, permanently, etc.). In this regard, it should be appreciated that each individual data value of received system data, such as the system data 302, need not be directly processed by the one or more models described herein.

[0104] As depicted, the system data 302 includes a plurality of sensor data associated with a target asset (e.g., asset A) and / or an associated asset (e.g., asset B). Specifically, the system data 302 includes asset A sensor data 302A, asset A sensor data 302B, and asset B sensor data 302C. The asset A sensor data 302A can embody received system data associated with a first sensor (e.g., a temperature sensor) of asset A. The asset A sensor data 302B can embody received system data associated with a second sensor (e.g., a vibration sensor) of asset A. The asset B sensor data 302C can embody received system data associated with a first sensor of asset B, such as where asset B is an upstream asset or a downstream asset relative to asset A.

[0105] It should be understood that in some embodiments, data from a single source is processed, such as where asset A sensor data 302A is processed only by one or more specific models for the purpose of determining one or more remaining life values. In other embodiments, multiple data from different sources are processed, such as where data from multiple sensors of a particular asset is processed (e.g., asset A sensor data 302A and asset A sensor data 302B), and / or data from multiple sensors associated with different assets is processed (e.g., asset A sensor data 302A and asset B sensor data 302C). In some such embodiments, additional system data (e.g., 302B and 302C) may not be received and / or processed and is otherwise optional.

[0106] System data 302 may be processed by one or more models 306. Models 306 may be stored and / or otherwise maintained by life monitoring device 200. Alternatively or additionally, in some embodiments, models 306 may be accessed by life monitoring device 200 without storing models 306 directly on life monitoring device 200. In some embodiments, models 306 are trained by a separate computing device and stored to life monitoring device 200 for subsequent use. In other embodiments, one or more models 306 are trained by life monitoring device 200.

[0107] Models 306 may include one or more models configured to detect anomalies in the operation of a target asset and / or generate one or more remaining life values ​​associated with the operation of the target asset. For example, in some embodiments, models 306 include a first model configured to detect at least one anomaly in the operation of the target asset from system data 302. The first model may embody a machine learning model, an algorithmic model, and / or a statistical model, or a combination thereof, specifically trained to identify such anomalies from system data 302. In some embodiments, the first model configured for anomaly detection may be specifically trained based on a database associated with the target asset and / or similar assets indicating instances in which anomalies have occurred in the operation of the target asset. Each identified anomaly in the operation of the target asset may be identified as being associated with one or more root cause variables corresponding to factors that influenced or otherwise caused the anomaly in the operation. In some embodiments, the root cause variables for a particular identified anomaly are predetermined. In other embodiments, at least one of models 306 (e.g., the first model for anomaly detection or an additional associated model) determines the root cause variables associated with the identified anomaly in the operation of the target asset.

[0108] The model 306 can additionally or alternatively include one or more models configured to generate one or more life remaining values based on the identified at least one anomaly and / or corresponding root cause variable(s). For example, in some embodiments, the model 306 includes a second model configured to generate a first life remaining value representing a time at which a value of a particular root cause variable reaches a limit threshold. In this regard, the second model can be utilized to generate a first remaining life value for each root cause variable associated with a detected anomaly. The remaining life value associated with a particular root cause variable can be generated by the second model based on a particular threshold, such as embodied by the variable limits 304. In this regard, a particular root cause variable can be associated with a particular limit threshold of the variable limits 304 representing an unacceptable limit of a particular measurable value associated with an operation of the target asset. Each threshold of the variable limits 304 can be predetermined, user-set, and / or determined by one or more algorithms, models, etc. The second model can utilize the variable limits 304 to generate a first remaining life value corresponding to each root cause variable based on a projected length of time until a value of the root cause variable reaches a limit threshold represented by the variable limits 304 corresponding to the root cause variable.

[0109] As shown, for example, the model 306 generates a first remaining life value for root cause X, specifically a root cause X remaining life value 308A, and generates a first remaining life value for root cause Y, specifically a root cause Y remaining life value 308B. The root cause X remaining life value 308A can embody a time interval until a value of a particular root cause X associated with a particular operational anomaly of the target asset, such as a temperature of the target asset, reaches a corresponding temperature threshold represented in the variable limits 304. The temperature threshold, such as represented by a first value in the variable limits 304, can embody an unacceptable temperature limit such that an operation of the asset above the temperature threshold is determined to be unacceptable. It should be understood that the model 306 can generate the root cause X remaining life value 308A based on historical system data associated with the root cause X, such as temperature, for example including the system data 302 and / or retrieved historical system data associated with the target asset. In this regard, in some embodiments, one or more particular portions of the system data are utilized to generate the root cause X remaining life value 308A, such as data values of data characteristics related only to a temperature of the target asset and / or associated upstream or downstream assets.

[0110] The root cause Y remaining life value 308B can embody a time interval until a value of a particular root cause Y associated with a particular operational anomaly of the target asset (e.g., vibration of the target asset) reaches a corresponding vibration threshold value represented in the variable limit 304. For example, the vibration threshold value represented by the second value in the variable limit 304 can embody an unacceptable vibration limit such that operation of the asset above the vibration threshold value is determined to be unacceptable. It will be appreciated that the model 306 can generate the root cause Y remaining life value 308B based on historical system data associated with the root cause Y (e.g., vibration), such as one or more portions of the system data 302 and / or retrieved historical system data associated with the target asset. It will be appreciated that the particular portions of system data relevant to the root cause X remaining life value 308A and the particular portions of system data relevant to the root cause Y remaining life value 308B can be different. For example, data from a first sensor or a first set of sensors can be processed or highly weighted as relevant for generating the root cause X remaining life value 308A, and data from a second sensor or a second set of sensors can be processed or highly weighted as relevant for generating the root cause Y remaining life value 308B.

[0111] Additionally or alternatively, the model 306 can include one or more models configured to generate a second remaining life value representing a remaining useful life of the target asset. For example, the model 306 can process at least the system data 302 to generate a health index remaining life value 310. The health index remaining life value 310 can represent the remaining useful life of the target asset based on a combination of root cause variables, such as root cause X and root cause Y. In some embodiments, the health index remaining life value 310 representing the remaining useful life is based on a limit threshold of an asset health index, which if not met, indicates that the target asset will no longer operate as intended or expected. For example, the asset health index threshold can be maintained by the life monitoring device 200 for comparison to a value of the asset health index at one or more times.

[0112] In some embodiments, the same model utilized to generate the remaining life value for one or more root cause variables is utilized to generate the second remaining life value representing the remaining useful life. For example, in some embodiments, an integrated model is utilized to select the particular model to utilize in generating the root cause X remaining life value 308A and the health index remaining life value. In this regard, it will be appreciated that a single model can be trained and utilized to generate each of the first remaining life value and the second remaining life value.

[0113] Figure 3B A visualization of an example computing environment for generating a root cause limit threshold for a root cause variable is shown in accordance with at least some example embodiments of the present disclosure. With respect to Figure 3BThe exemplary computing environment depicted may similarly be maintained by the life monitoring device 200. In this regard, the life monitoring device 200 may perform operations relative to Figure 3B Describes the various data processing and interactions to generate values ​​that represent root cause limit thresholds for specific root cause variables.

[0114] As shown, one or more degradation models 356 are utilized to generate a first level limit threshold 358. The degradation model 356 receives inputs including known failure limits 352 and real-time system data 354. The known failure limits 352 may represent determined and / or otherwise known values ​​of specific root cause variables that, if exceeded, may cause the target asset to fail (e.g., cease operation in a standard manner and as intended). The known failure limits 352 may be determined based on previously received system data associated with the operation of the target asset, user input indicating the known failure limits 352, and the like. For example, in some embodiments, historical data maintained by or otherwise accessible to the life monitoring device 200, measured during a time when the known asset and / or associated assets were operating as intended (e.g., immediately following maintenance on one or more assets), is accessed to calculate known failure limits 352 for one or more individual root cause variables and / or an asset health index, e.g., embodying a combination of one or more root cause variables.

[0115] Real-time system data 354 may be received and / or otherwise measured from the target asset and / or associated assets. For example, in some embodiments, the real-time system data 354 is received in real time from sensors in or operably coupled to the target asset for measuring specific data values ​​associated with the operation of the target asset. In some embodiments, the real-time system data 354 is processed to generate subsets of the real-time system data 354 that are determined to be similar based on one or more shared characteristics. In this regard, the system data 354 may be subsets such that similar data is used for subsequent operations, such as for generating root cause variable limit thresholds and / or for generating life remaining values ​​as described herein. In one exemplary context, for example, the real-time system data 354 is a subset based on the operating mode of the target asset.

[0116] The degradation models 356 may include any number of possible models for capturing different types of degradation. For example, as shown, the degradation models 356 include a linear degradation model 356A, a nonlinear degradation model 356B, and an exponential degradation model 356C. It should be understood that the degradation models 356 may include any number of degradation models configured to account for different types of degradation. One of the degradation models 356 may be selected for use in generating the root cause limit threshold, for example, based on determining which degradation model 356 is associated with the most accurate fit to the test data.

[0117] In some embodiments, a determination is made as to which degradation model 356 to utilize for each newly received batch of real-time system data 354. In this regard, for different batches of real-time system data 354 (e.g., different batches of temperature data) associated with the same root cause variable, different types of degradation models 356 may be selected for use. For example, in some embodiments, a particular degradation model or type of degradation model may be updated to whichever best fits the trend of the value of the root cause variable, such that as the trend of the actual value of the root cause variable is updated, the corresponding selected type of degradation model that best fits the updated trend may be selected.

[0118] The selected degradation model of the degradation models 356 can be used to generate a first level limit threshold 358 associated with the root cause variable. In some embodiments, the first level limit threshold 358 is used as the root cause limit threshold for the particular root cause variable without further processing. In other embodiments, the first level limit threshold 358 is further processed to generate an adjusted final limit threshold 364 for use as the root cause limit threshold for the root cause variable.

[0119] As shown, for example, in some embodiments, the first level limit threshold 358 is further processed using one or more life models 362 to generate an adjusted final limit threshold 364 for use. In some embodiments, the first level limit threshold 358 is used as input along with historical failure event data 360 from the life models 362 to generate the adjusted final limit threshold 364. In this regard, the historical failure event data 360 embodies data indicating instances in which the target asset has failed, as well as corresponding values ​​of one or more root cause variables during such events. In this regard, the life models 362 can adjust the first level limit threshold 358 based on the historical failure event data 360 to account for specific data values ​​of the root cause variables and / or other root cause variables in the historical failure event data 360.

[0120] In some embodiments, for example, the first level extreme threshold 358 and the historical failure event data 360 are processed by a reliability survival model 362A. The reliability survival model 362A may embody one or more statistical, algorithmic, and / or machine learning models configured to determine a value of a root cause variable that, if exceeded (or in other cases, if the value drops below), determines that the target asset is unlikely to perform reliably. For example, the reliability survival model 362A may determine whether the first level extreme threshold 358 adequately captures all or a specific target percentage of failure events embodied in the historical failure event data 360, and if not, adjust the extreme threshold. In this regard, in some embodiments, the first level extreme threshold 358 may be adjusted or set to be equal to the extreme threshold determined via the reliability survival model 362A based on the historical failure event data 360 and the first level extreme threshold 358.

[0121] In some embodiments, for example, the first level limit threshold 358 and the historical failure event data 360 are processed by a covariate survival model 362B. The covariate survival model 362B may embody one or more statistical, algorithmic, and / or machine learning models configured to determine and adjust the first level limit threshold 358 for a root cause variable based on a relationship between the root cause variable and one or more other root cause variables. In this regard, for example, the covariate survival model 362B may determine, based on at least the historical failure event data 360, that the limit threshold for a root cause variable should be adjusted based on a relationship with a second root cause variable. In one particular context, a direct relationship may be determined that indicates that as the value of the first root cause variable increases, the target asset is more likely to fail at lower values ​​of the second root cause variable. Such relationships between multiple root cause variables may be determined by the covariate survival model 362B for any number of root cause variables and / or combinations of root cause variables.

[0122] In some embodiments, the lifespan monitoring device 200 stores and / or otherwise maintains an adjusted final limit threshold value 364 associated with a corresponding root cause variable. In this regard, the lifespan monitoring device 200 may store the adjusted final limit threshold value 364 as the root cause limit threshold value for the corresponding root cause variable. For example, the lifespan monitoring device 200 may utilize the stored adjusted final limit threshold value 364 to determine a first remaining lifespan value representing a time at which the root cause limit threshold value associated with the root cause variable is reached, and / or a second remaining lifespan value representing a remaining useful life of the target asset based on a health index associated with the target asset.

[0123] Figure 3CA visualization of an exemplary computing environment for generating remaining life values ​​for root cause variables according to at least some exemplary embodiments of the present disclosure is shown. As depicted, remaining life values ​​may be generated based on and otherwise associated with root cause limit thresholds for particular root cause variables. For example, as described herein with respect to Figure 3B As described, the variable limit 390 for a particular root cause variable may be represented by the adjusted final limit threshold 364 or the first level limit threshold 358 for the root cause variable.

[0124] As shown, a subset of time series data is received that passes through a selected schema 370. The time series data may represent received system data associated with a particular target asset over one or more time intervals. In some embodiments, the system data includes multiple measurements from a particular sensor measured at different points in time and associated with one or more root cause variables. The time series data may be partitioned into subsets associated with similar data values, for example, by partitioning the time series data based on the selected schema of the asset. For example, the time series data subset may be partitioned by the selected schema of the target asset.

[0125] The real-time data 372 of the time series data subset divided by the selected pattern 370 can be processed in one or more operations as depicted and described. For example, in some embodiments, the life monitoring device 200 performs a prediction error determination operation 382. For example, at the prediction error determination 382, ​​the life monitoring device 200 compares the predicted value of the root cause variable at a particular time with the actual value of the root cause variable identified in the real-time data 372 or determined based on the real-time data. In this regard, a prediction error 384 is determined based on the difference between the predicted value of the root cause variable and the actual value of the root cause variable according to the operation 382. In this regard, the prediction error 384 can be recycled to update the fit of the corresponding model relative to modeling the actual trend of the root cause variable. Alternatively or additionally, the prediction error 384 can be used to adjust the next predicted value.

[0126] One or more data inputs may be used to calculate one or more models (e.g., Figure 3BThe goodness of fit of the degraded model 356 (as depicted and described above) can be determined. In this regard, the degraded model determined to be associated with the best fit for a particular root cause variable can be selected for use in generating the next estimated value for the root cause variable. A value representing the goodness of fit calculated for the particular degraded model can further be provided, for example, for review by a user, to enable the user to consider the probability that the remaining life value is accurate, as represented by the model's goodness of fit value. In some embodiments, such as using an ensemble model, the life monitoring device 200 selects a selected model 388 based on the determined goodness of fit value for each candidate model.

[0127] The selected model 388 may be stored and / or otherwise archived in the model storage device 376 upon selection. In some embodiments, the selected model 388 may be stored in association with a pattern attribute indicating a selected pattern for the target asset. In this regard, the model may be stored along with an indication of the selected pattern 374 for use in recreating or otherwise "rehydrating" previous system data processed by the corresponding selected model 388. For example, an archived version of the selected model 388 may be used to recreate a batch of old data processed by the selected model 388 for further processing. In this regard, the life monitoring device 200 may utilize the archived version of the model to recreate the old system data at operation 378, such that the recreated old data 380 may be merged with a new batch of real-time data 372 to increase the data size used to create the model and improve the accuracy of the model across a wider database. In this regard, each iteration of model generation, selection, and / or processing improves the accuracy and robustness of the model fit to the received data. Additionally or alternatively, such embodiments better capture changes in trend curves by utilizing an integration of one or more such models, for example, a change from a linear value change during normal operation to an exponential value change during degradation.

[0128] Additionally or alternatively, in some embodiments, the model receives prediction errors 384 as input. Prediction errors 384 can provide adjustments to the remaining life values ​​generated for the next batch of real-time data 372. In this regard, errors in previously generated remaining life values ​​are recycled to improve the accuracy of subsequent iterations of the estimate. Such embodiments of the present disclosure adapt to prediction errors to improve the overall accuracy of subsequent predictions.

[0129] One or more data values ​​are then generated using the selected model 388. In some embodiments, for example, the selected model 388 is used to generate estimated values ​​of the root cause variable at one or more future times. In some such embodiments, the estimated values ​​of the root cause variable represent or are otherwise used to derive a first remaining life value 392 for the root cause variable. The first remaining life value 392 may represent a time until the value of the root cause variable is estimated to reach or exceed a particular root cause limit threshold represented by the variable limit 390. As described herein, the variable limit 390 may be as described herein with respect to Figure 3B The threshold value 364 is generated as described, for example, as embodied by the adjusted final limit threshold value 364 .

[0130] In some embodiments, the first remaining life value 392 is used to provide one or more user interfaces 394. For example, the first remaining life value 392 can be provided to a user via one or more user interfaces 394 so that the user can view and / or process the first remaining life value 392. In one exemplary context, the user can process the user interface 394 to determine when to initiate one or more maintenance operations associated with a particular target asset. In some embodiments, for example, as described herein with respect to Figure 4 and / or Figure 5 As depicted, the first remaining life value 392 is presented to one or more user interfaces.

[0131] Relative to Figure 3C The processing operations described may be similarly performed with respect to an asset health index. In this regard, the asset health index may reflect a combination of root cause variables that each impact the operational health of the target asset. For example, a selected model may be utilized to receive and process real-time data associated with one or more sensors. For example, the selected model may be selected based on a best fit to a previous value of the asset health index. In this regard, real-time system data and rebuilt system data from previously utilized models may be utilized to generate and / or select the selected model 388 for further processing. The selected model may be used to generate a second remaining useful life value representing the remaining useful life of the overall target asset. For example, in some embodiments, as described herein with respect to Figure 3B In this regard, the remaining useful life represented by the second remaining useful life value may represent an estimated length of time until the value of the asset health index violates (e.g., falls below or, in other embodiments, rises above) the corresponding limiting threshold. For example, based on a combination of root cause variables, the second remaining useful life value representing the remaining useful life of the target asset may similarly be provided via one or more user interfaces 394. For example, in some embodiments, via a user interface as described herein with respect to Figure 4 and / or Figure 5 One or more user interfaces are described that provide a second remaining life value.

[0132] Exemplary user interfaces of the present disclosure

[0133] Figure 4 An exemplary user interface for providing a remaining life value associated with an asset health index of an asset or system according to at least some exemplary embodiments of the present disclosure is shown. The exemplary user interface 400 can be presented by one or more computing devices, such as the life monitoring apparatus 200, which can cause the exemplary user interface 400 to be presented to a display or one or more client devices associated with the life monitoring apparatus 200. For example, in some embodiments, the exemplary user interface 400 is presented to a client device associated with a particular user, so that the user can view and / or otherwise interact with the exemplary user interface 400.

[0134] The exemplary user interface 400 includes a plurality of interface elements associated with monitoring the operational life of a target asset. For example, as shown, the exemplary user interface 400 includes a remaining useful life graph 420. The remaining useful life graph 420 depicts a visualization of a second remaining useful life value associated with the target asset, which represents the estimated remaining useful life of the target asset over a specific time interval. It should be understood that, as described herein, the remaining useful life associated with the target asset may be generated by one or more models specifically configured to generate the remaining useful life based on, for example, system data associated with the target asset.

[0135] As depicted, for example, the remaining useful life graph 420 includes a remaining useful life value line 404. The remaining useful life value line 404 represents the generated remaining useful life value of the target asset within a specific time period. Specifically, as shown, the remaining useful life graph 420 includes a remaining useful life value line 404, which represents the remaining useful life of the target asset within the time interval from the starting timestamp represented by time 402 (e.g., reflecting the current timestamp) to the ending timestamp represented by time mark 406 (e.g., reflecting the time until the remaining useful life of the target asset has passed). In this regard, the remaining useful life represented by the remaining useful life value line 404 can be associated with the value of the health index associated with the target asset. For example, in some embodiments, the remaining useful life value reflects the length of time until the value of the health index associated with the target asset reaches a specific threshold. As shown, for example, the remaining useful life value line 404 represents the remaining length of time until the asset health index associated with the target asset is estimated to exceed the asset health index threshold depicted by the asset health threshold indicator 408. In this regard, a user may view the remaining useful life graph 420 to easily identify the remaining useful life of the target asset and / or identify expected changes in the remaining useful life of the target asset over time.

[0136] Additionally or alternatively, in some embodiments, the remaining useful life plot 420 and / or associated interface elements can be presented that indicate actions to be performed in association with the target asset. For example, in some embodiments, the remaining useful life plot 420 can be presented that includes one or more interface elements that include or otherwise embody a maintenance notification that indicates that maintenance of the target asset should be performed at a particular timestamp (e.g., at a particular offset from the value of the remaining life reaching zero or another threshold). In this regard, the maintenance notification can provide a notification to the user that maintenance should be performed at a particular time based on the estimated actual degradation in operation of the target asset.

[0137] As depicted, the example user interface 400 also includes one or more interface elements associated with root cause variables that contribute to the generated remaining life value indicative of the remaining useful life of the target asset. For example, the example user interface 400 includes a root cause contribution plot 410 that indicates values associated with root cause variables that contribute to the operational health of the target asset. In this regard, the root cause contribution plot 410 can include interface elements associated with each root cause variable that display the value of the root cause variable, the influence of which on the asset health index is indicative of the overall operational health of the target asset based on a combination of root cause variables, etc. A user can view the root cause contribution plot 410 to identify individual root cause variables that have the greatest influence on the operational health of the target asset. Alternatively or additionally, in some embodiments, the root cause contribution plot 410 is configured to receive a user interaction associated with a particular root cause variable and, in response to the user interaction, cause presentation of one or more additional interfaces associated with the root cause variable.

[0138] Figure 5 Another example user interface is shown that provides a first remaining life value associated with a particular root cause variable of an asset or system, in accordance with at least some example embodiments of the present disclosure. Specifically, Figure 5 An example user interface 500 is depicted that represents a first remaining life value associated with a particular root cause variable. In one example context, the root cause variable can represent, for example, a temperature value associated with the operation of a target asset, a vibration value, etc.

[0139] As depicted, the example user interface 500 includes a root cause variable value line 504. The root cause variable value line 504 depicts a calculated and / or otherwise generated value of the root cause variable based on sensor data at a particular time. Specifically, the root cause variable value line 504 begins at a start time 508, which represents, for example, a time at which the target asset began operation after a most recently performed maintenance operation.

[0140] A root cause variable value line 504 is depicted along with a representation of a corresponding root cause variable threshold value 502. Root cause variable threshold value 502 indicates an extreme threshold value for the value of the root cause variable, e.g., such that if the value of the root cause variable exceeds root cause variable threshold value 502, the operation of the target asset may be negatively impacted and maintenance of the target asset may be recommended or required. Root cause variable threshold value 502 may be user-set and automatically determined based on historical data associated with the operation of the target asset (e.g., historical system data associated with the target asset).

[0141] As depicted, the root cause variable value represented by root cause variable value line 504 exceeds the root cause variable threshold value 502 at the second particular time. Specifically, the value represented by root cause variable value line 504 exceeds the root cause variable threshold value 502 at time marker 510. In this regard, it is determined that the actual value of the root cause variable represented by root cause variable value line 504 exceeds the corresponding threshold value at the second time marker. In some embodiments, the life monitoring device 200 generates an actual value of the root cause variable upon receiving system data and compares the actual value of the root cause variable to the root cause variable threshold value to determine whether the root cause variable threshold value has been exceeded.

[0142] The user interface 500 also includes a first remaining life value line 506 corresponding to a first remaining life value of the root cause variable. In this regard, the first remaining life value line 506 may indicate an estimated remaining life value of the target asset until the value of the root cause variable associated with the target asset exceeds the root cause variable threshold 502. In this regard, the first remaining life value line 506 and the root cause variable value line 504 depict generally opposite trends to one another—such that as the value of the root cause variable represented by the root cause variable value line 504 increases, the first remaining life value represented by the first remaining life value line 506 generally decreases as time passes until the root cause variable threshold 502 is reached.

[0143] In some embodiments, a user accesses the user interface 500 via user input associated with one or more of the interface elements depicted and described with respect to the user interface 400. In this regard, some such embodiments of the present disclosure provide the user with a state-of-the-art interface that enables the user to easily identify the remaining useful life of a particular target asset without performing the excessive manual calculations traditionally required to determine the remaining useful life of the target asset based on one or more contributing factors. For example, a user may view and / or interact with the user interface 400 to perform one or more determinations based on an aggregated asset health index associated with the target asset and / or otherwise monitor the operational health of one or more assets. Furthermore, in the event that the user desires to view and / or utilize information associated with a particular aspect of the operational health of the target asset, the user may interact with a particular interface element to access a user interface associated with the desired particular aspect. In this regard, the life monitoring device 200 may provide high-level information to enable the user to quickly perform high-level determinations that may be of interest to the user regarding the operational health of the particular asset, while also enabling the user to "drill down" into specific aspects of the high-level information to conduct a narrower investigation into the operational health of the particular target asset and / or future root causes of issues related to the operation.

[0144] Exemplary Processes of the Disclosure

[0145] Having described the exemplary systems, devices, computing environments, and user interfaces associated with the embodiments of the present disclosure, it will now be discussed exemplary flow charts including various operations performed by the devices and / or systems described herein. It should be understood that each flow chart in the flow chart depicts an exemplary computer-implemented process that can be performed by one or more of the devices, systems, and / or equipment described herein, such as by one or more of its components. The blocks indicating the operation of each process can be arranged in any of a variety of ways, as depicted and described herein. In some such embodiments, one or more blocks of any process in the process described herein occur between one or more blocks of another process, before one or more blocks of another process, and / or otherwise operate as subprocesses of a second process. Additionally or alternatively, any process in the process may include some or all of the steps described and / or depicted, including one or more optional operation blocks in some embodiments. With respect to the following flow charts, in some or all embodiments of the present disclosure, one or more blocks in the depicted blocks may be optional. Optional blocks are shown with dotted lines (or "dash-dot lines"). Similarly, it should be understood that one or more of the operations of each flow diagram may be combined, replaced, and / or otherwise varied as described herein.

[0146] Figure 6A flow chart is shown including operational blocks of an exemplary process for monitoring the remaining useful life of an asset, according to at least some exemplary embodiments of the present disclosure. Specifically, Figure 6 The operations of the exemplary process 600 are described. In some embodiments, the computer-implemented process 600 is embodied by computer program code stored on a non-transitory computer-readable medium of a computer program product, the computer program code being configured to be executed to perform the computer-implemented method. Alternatively or additionally, in some embodiments, the exemplary process 600 is performed by one or more specially configured computing devices, such as the life monitoring system 102 embodied by the specially configured life monitoring device 200. In this regard, in some such embodiments, the life monitoring device 200 is specially configured by computer program instructions stored on the device, such as in the memory 204 and / or another component depicted and / or described herein, and / or otherwise accessible to the life monitoring device 200, for performing the operations depicted and described with respect to the exemplary process 600. In some embodiments, the specially configured life monitoring device 200 includes and / or otherwise communicates with one or more external devices, systems, equipment, etc., to perform one or more of the depicted and described operations.

[0147] Process 600 begins at operation 602. At operation 602, life monitoring device 200 includes devices such as input / output circuitry 206, communication circuitry 208, data monitoring circuitry 210, life monitoring circuitry 212, processor 202, etc., to receive system data associated with an operating system including a target asset. The system data may be received from one or more sensors (such as sensors associated with the target asset) and / or in some embodiments, from other assets upstream or downstream of the target asset. Additionally or alternatively, in some embodiments, at least a portion of the system data is received directly from the target asset or an upstream asset or a downstream asset, and / or from a controller of another computing device that communicates with the target asset or an upstream asset or a downstream asset of the target asset. For example, in some embodiments, configuration data reflecting the currently selected mode of the target asset is received from the target asset itself or a controller of the target asset. In some embodiments, the system data is received in real time continuously or at defined time intervals (e.g., every minute, every 5 minutes, every hour, etc.).

[0148] At operation 604, the life monitoring device 200 includes devices such as input / output circuitry 206, communication circuitry 208, data monitoring circuitry 210, life monitoring circuitry 212, processor 202, etc., to determine at least one operational anomaly associated with the operating system based on the received system data. The operational anomaly may indicate that at least one aspect of the operation of a particular asset or an associated upstream asset or downstream asset is different from standard operation. In this regard, the life monitoring device 200 may process the system data associated with the particular asset or a specific portion thereof to, for example, determine at least one operational anomaly associated with the target asset. Non-limiting examples of operational anomalies include one or more variables associated with: operation of the asset reaching unusual levels (e.g., high or low temperatures based on a temperature anomaly threshold, high or low vibration levels based on a vibration anomaly threshold, etc.), loss of communication with the asset (e.g., asset downtime), changes in the operating speed of the asset, etc.

[0149] In some embodiments, the life monitoring device 200 determines at least one operational anomaly using a model configured to determine such an operational anomaly for one or more assets. For example, the model can be a specially trained algorithm, machine learning, and / or statistical model configured to determine an operational anomaly for a particular asset based on specific training data indicative of such an operational anomaly. In some such embodiments, the model is trained based on data associated with the operation of the particular asset during standard operating hours.

[0150] At operation 606, life monitoring device 200, including devices such as input / output circuitry 206, communication circuitry 208, data monitoring circuitry 210, life monitoring circuitry 212, processor 202, and the like, identifies at least one root cause variable associated with at least one operational anomaly. In some embodiments, the at least one root cause variable is determined by determining a portion of system data that falls outside a normal operating region (e.g., exceeds an operational threshold) when the operational anomaly is detected. In other embodiments, a particular detected type of anomaly corresponds to one or more particular root cause variables.

[0151] At operation 608, the life monitoring device 200 includes devices such as the input / output circuit 206, the communication circuit 208, the data monitoring circuit 210, the life monitoring circuit 212, the processor 202, etc., to generate a first remaining life value associated with a first root cause variable of the at least one root cause variable. The generated first remaining life value reflects a value representing the length of time until the value associated with the corresponding root cause variable of the at least one root cause variable reaches a specific limit threshold of the root cause variable. In some embodiments, the first remaining life value corresponds to a target asset and indicates an estimated length of time until the value associated with the root cause variable associated with the target asset reaches the specific root cause threshold. In some embodiments, for example, the root cause variable reflects a specific type of monitoring data associated with the target asset (e.g., a physical characteristic monitored by one or more sensors of the target asset or an associated upstream or downstream asset, such as temperature or vibration level). In some embodiments, the life monitoring device 200 generates the first remaining life value for each root cause variable of the at least one root cause variable.

[0152] One or more models may be utilized to generate the first remaining life value. In some embodiments, for example, one or more specifically trained machine learning, algorithmic, and / or statistical models are utilized to generate the first remaining life value based on some or all of the system data. In some embodiments, for example, a model is selected from a plurality of models for use in generating the first remaining life value, for example, via an ensemble process. In this regard, various portions of the system data may be processed by different one or more models, and the model with the best fit may be selected to be associated with the particular root cause variable for generating the first remaining life value for the particular root cause variable. In this regard, the model determined to be the most accurate or otherwise optimal for each particular root cause variable may be utilized.

[0153] At operation 610, the life monitoring device 200, including devices such as the input / output circuitry 206, the communication circuitry 208, the data monitoring circuitry 210, the life monitoring circuitry 212, the processor 202, and the like, generates a second remaining life value associated with the asset health index. The second remaining life value may represent an estimated overall remaining useful life of the target asset based on any number of root cause variables that affect the life of the target asset. The asset health index may be associated with one or more of the at least one root cause variable. In this regard, the asset health index may indicate the overall operational health of the target asset, for example, based on a combination of the one or more root cause variables that affect the asset health index. A value representing the asset health index may be generated and / or derived based on the value of each individual root cause variable that affects the asset health index.

[0154] One or more models can be utilized to generate the second remaining life value associated with the asset health index. In some embodiments, the second remaining life value associated with the asset health index is generated utilizing the integration process described above with respect to each individual root cause variable. For example, a model utilized to generate the first remaining life value corresponding to a particular root cause variable can similarly be utilized to generate the second remaining life value associated with the asset health index.

[0155] At operation 612, the life monitoring device 200 includes means, such as the input / output circuit 206, the communication circuit 208, the data monitoring circuit 210, the life monitoring circuit 212, the processor 202, etc., to provide at least the second remaining life value. In this regard, the life monitoring device 200 can provide at least the second remaining life value for further processing and / or display. In some embodiments, for example, the life monitoring device 200 provides the second remaining life value for presentation via one or more displayed user interfaces. Such user interfaces can be presented via a display of the life monitoring device 200 and / or a client device associated with the life monitoring device 200. In this regard, for example, in some embodiments, the life monitoring device 200 transmits the second remaining life value to a client device for presentation in order to provide the second remaining life value. In some embodiments, additionally or alternatively, the life monitoring device 200 provides the second remaining life value in addition to or in lieu of the first remaining life value for one or more of the at least one root cause variable, such as in order to present a user interface including one or more elements representing or based on the second remaining life value corresponding to the asset health index of the target asset and / or one or more interface elements representing or based on the first remaining life value of one or more root cause variables affecting the asset health index.

[0156] In some embodiments, the life monitoring device 200 is configured to perform one or more actions based on the at least second remaining life value. For example, at optional operation 614, the life monitoring device 200 includes devices such as the input / output circuitry 206, the communication circuitry 208, the data monitoring circuitry 210, the life monitoring circuitry 212, the processor 202, and / or the like to initiate a maintenance notification based on at least the second remaining life value. In this regard, the maintenance notification can include information indicating that maintenance of the target asset and / or one or more associated assets (such as an upstream asset and / or a downstream asset) should be performed within a particular time or a particular time interval. In some embodiments, the maintenance notification is initiated via one or more user interfaces presented via a user-facing application associated with the life monitoring device 200 (e.g., a user interface of a particular software application for accessing functionality of the life monitoring device 200 via the life monitoring device 200 or an associated client device). In other embodiments, the maintenance notification is initiated via a third-party application, such as by initiating a maintenance notification embodied by an email message, a text notification, a push notification, and / or the like.

[0157] In some embodiments, the maintenance notification is initiated based on at least a determination that the second remaining life value violates (e.g., falls below) a particular threshold. In this regard, the determination can indicate that a particular time difference until expiration of the remaining useful life represented by the second remaining life value falls below a particular threshold. For example, based on the second remaining life value, the life monitoring device 200 can identify a time representing an offset from expiration of the asset remaining useful life represented by the second remaining life value by a predetermined, user-configured, or otherwise determined length of time (e.g., X days, hours, and / or the like until a time at which the remaining useful life of the particular target asset reaches zero), and initiate the maintenance notification in the event that the life monitoring device 200 determines that a current time stamp is at or after that time. In this regard, the maintenance notification can be provided to prompt an associated user to perform maintenance on the target asset prior to expiration of the target asset’s remaining useful life, while similarly not prompting maintenance so early as to waste significant uptime by over-maintaining the target asset.

[0158] Figure 7 A flowchart illustrating additional operation blocks of an example process including generating at least one remaining life value based on a selected mode in monitoring asset remaining useful life values in accordance with at least some example embodiments of the present disclosure is shown. Specifically, Figure 7An exemplary process 700 for generating at least one remaining life value based on a selected mode is depicted. In some embodiments, the computer-implemented process 700 is embodied by computer program code stored on a non-transitory, computer-readable medium of a computer program product, the computer program code being configured to be executed to perform the computer-implemented method. Alternatively or additionally, in some embodiments, the exemplary process 700 is performed by one or more specially configured computing devices, such as the life monitoring system 102 embodied by a specially configured life monitoring device 200. In this regard, in some such embodiments, the life monitoring device 200 is specially configured by computer program instructions stored on the device, such as in the memory 204 and / or another component depicted and / or described herein, and / or otherwise accessible to the life monitoring device 200, to perform the operations depicted and described with respect to the exemplary process 700. In some embodiments, the specially configured life monitoring device 200 includes and / or otherwise communicates with one or more external devices, systems, equipment, etc., to perform one or more of the depicted and described operations.

[0159] Process 700 begins at operation 702. In some embodiments, process 700 begins after one or more operations of another process, such as operation 606 of process 600 as depicted and described. Additionally or alternatively, in some embodiments, upon completion of process 700, flow proceeds to one or more operations of another process, such as operation 608 of process 600 as depicted and described. In other embodiments, upon completion of process 700, flow ends.

[0160] At operation 702, the life monitoring device 200, including devices such as the input / output circuitry 206, the communication circuitry 208, the data monitoring circuitry 210, the life monitoring circuitry 212, the processor 202, and the like, determines that a target asset is utilizing a selected mode from a plurality of configurable modes. In some embodiments, received system data associated with the target asset includes one or more values ​​indicative of the selected mode utilized by the target asset. Such system data indicative of the selected mode utilized by the target asset may be received from the target asset, a controller or other computing device associated with the operation of the target asset, an upstream asset or downstream asset associated with the target asset, and / or one or more sensors associated with the target asset.

[0161] At operation 704, the life monitoring device 200 includes means, such as the input / output circuit 206, the communication circuit 208, the data monitoring circuit 210, the life monitoring circuit 212, the processor 202, etc., to generate the first remaining life value and / or the second remaining life value based on the selected mode. In some embodiments, for example, the selected mode is used to determine a particular model to utilize in generating the first remaining life value and / or the second remaining life value (e.g., to determine the model that is most accurate for the selected mode). Alternatively or additionally, in some embodiments, the system data used to generate the first remaining life value and / or the second remaining life value is based on the selected mode. For example, in some cases, only system data that is indicated as corresponding to the selected mode is processed. In this regard, it will be appreciated that by generating the first remaining life value and / or the second remaining life value based on the selected mode, the generated value can be more accurate with respect to the operation of the target asset in the selected mode.

[0162] Figure 8 A flowchart illustrating additional operation blocks of an example process including for providing a trend bias of at least one root cause variable in monitoring asset remaining useful life values is shown in accordance with at least some example embodiments of the present disclosure. Specifically, Figure 8 An example process 800 for providing a trend bias of at least one root cause variable is depicted. In some embodiments, the computer-implemented process 800 is embodied by computer program code stored on a non-transitory computer-readable medium of a computer program product that is configured for execution to perform a computer-implemented method. Alternatively or additionally, in some embodiments, the example process 800 is performed by one or more specially configured computing devices, such as the life monitoring system 102 embodied by a specially configured life monitoring device 200. In this regard, in some such embodiments, the life monitoring device 200 is specially configured by computer program instructions stored on the device, for example in the memory 204 and / or another component depicted and / or described herein and / or otherwise accessible to the life monitoring device 200, to perform the operations depicted and described with respect to the example process 800. In some embodiments, the specially configured life monitoring device 200 includes and / or otherwise communicates with one or more external devices, systems, apparatuses, etc., to perform one or more of the depicted and described operations.

[0163] Process 800 begins at operation 802. In some embodiments, process 800 begins after one or more operations of another process, such as operation 612 of process 600 as depicted and described. Additionally or alternatively, in some embodiments, upon completion of process 800, the flow proceeds to one or more operations of another process, such as operation 614 of process 600 as depicted and described. In other embodiments, upon completion of process 800, the flow ends.

[0164] At operation 802, the life monitoring device 200, including devices such as the input / output circuitry 206, the communication circuitry 208, the data monitoring circuitry 210, the life monitoring circuitry 212, and the processor 202, provides at least one deviation between an expected trend of at least one root cause variable and an actual trend of the at least one root cause variable. In this regard, for example, an estimate of a root cause value at a future time and represented by a first remaining life value may be stored after the value is generated. Subsequent actual system data associated with the subsequent time is received and processed to determine an actual value of the root cause variable at that time. Based on the actual value and the previously generated and stored expected value, a deviation may be generated at a particular time. It should be understood that this process may be repeated any number of times, with the deviation representing the difference between the expected trend and the actual trend over time.

[0165] In some embodiments, the life monitoring device 200 provides at least one deviation between an expected trend and an actual trend of at least one root cause variable for presentation. For example, the life monitoring device 200 may cause the presentation of one or more interface elements depicting the deviation between the expected trend and the actual trend. In this regard, such interface elements may visually depict the offset between the actual trend and the expected trend over time. In this regard, a user may view and / or process at least one deviation between trends to inform the user of the accuracy of a previous estimate of at least one root cause variable. Similarly, such data may inform the user whether the estimated remaining life value of at least one root cause variable is likely to remain accurate. For example, in the case where the deviation between the estimated trend and the actual trend of a particular root cause variable is determined to be generally large, the estimated remaining life value of the root cause variable may be less credibly accurate than in the case where the deviation between the estimated trend and the actual trend of the particular root cause variable is determined to be generally small.

[0166] Figure 9 A flow chart illustrating additional operational blocks of an exemplary process for providing a first remaining useful life value of at least one root cause variable when monitoring an asset's remaining useful life value is shown, according to at least some exemplary embodiments of the present disclosure. Specifically, Figure 9An exemplary process 900 for providing a first remaining life value for at least one root cause variable is depicted. In some embodiments, the computer-implemented process 900 is embodied by computer program code stored on a non-transitory, computer-readable medium of a computer program product, the computer program code being configured to be executed to perform the computer-implemented method. Alternatively or additionally, in some embodiments, the exemplary process 900 is performed by one or more specially configured computing devices, such as the life monitoring system 102 embodied by a specially configured life monitoring device 200. In this regard, in some such embodiments, the life monitoring device 200 is specially configured to perform the operations depicted and described with respect to the exemplary process 900 by computer program instructions stored on the device, such as in the memory 204 and / or another component depicted and / or described herein, and / or otherwise accessible to the life monitoring device 200. In some embodiments, the specially configured life monitoring device 200 includes and / or otherwise communicates with one or more external devices, systems, equipment, etc., to perform one or more of the depicted and described operations.

[0167] Process 900 begins at operation 902. In some embodiments, process 900 begins after one or more operations of another process, such as operation 612 of process 600 as depicted and described. Additionally or alternatively, in some embodiments, upon completion of process 900, flow proceeds to one or more operations of another process, such as operation 614 of process 600 as depicted and described. In other embodiments, upon completion of process 900, flow ends.

[0168] At operation 902, the life monitoring device 200 includes devices such as input / output circuitry 206, communication circuitry 208, data monitoring circuitry 210, life monitoring circuitry 212, processor 202, etc., to receive user input requesting display of a first remaining life value for at least a first root cause variable of at least one root cause variable. In some embodiments, for example, a user may interact with one or more interface elements of a presented user interface corresponding to a first root cause variable of the at least one root cause variable. Such interaction may indicate that the user desires to explore more details regarding a particular root cause variable. For example, in some embodiments, the user interface includes one or more interface elements associated with an asset health index and / or an aggregate of second remaining life values ​​of target assets based on the asset health index, as well as interface elements associated with each or at least some root cause variables associated with the asset health index. The user may interact with an interface element associated with a particular root cause variable to request display of additional information associated with the particular root cause variable, such as display of at least the first remaining life value associated with the root cause variable. A user may provide such user input to individually address and / or otherwise investigate the operational health of a target asset, and / or address and / or otherwise investigate the impact of a particular root cause variable on an asset health index and / or a corresponding second remaining useful life value representing the overall remaining useful life of the target asset.

[0169] At operation 904, the life monitoring device 200, including devices such as the input / output circuitry 206, the communication circuitry 208, the data monitoring circuitry 210, the life monitoring circuitry 212, the processor 202, and the like, presents a representation of the first remaining life value. In this regard, for example, the life monitoring device 200 may present an additional or alternative user interface that presents estimated remaining life values ​​for the root cause variables over time. The first remaining life value may be presented along with a representation of the corresponding root cause threshold associated with the first remaining life value. In this regard, it should be understood that in addition to processing the overall asset health index and the associated second remaining life value corresponding to the remaining useful life of the target asset based on a combination of one or more root cause variables, a user may individually explore any number of the root cause variables.

[0170] in conclusion

[0171] Although an exemplary processing system has been described above, specific implementations of the subject matter and functional operations described herein may be realized in other types of digital electronic circuitry, or in computer software, firmware, or hardware (including the structures disclosed in this specification and their structural equivalents), or in combinations of one or more of them.

[0172] Embodiments of the subject matter and operations described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described herein can be implemented as one or more computer programs (i.e., one or more modules of computer program instructions) encoded on a computer storage medium for execution by, or to control the operation of, information / data processing apparatus. Alternatively or additionally, the program instructions can be encoded on a propagated signal that is generated to encode information / data for transmission to a suitable receiver apparatus for execution by information / data processing apparatus. A computer storage medium can be, or include, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or include, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).

[0173] The operations described herein can be implemented as operations performed by an information / data processing apparatus on information / data stored on one or more computer-readable storage devices or received from other sources.

[0174] The term“data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones of the foregoing. The apparatus can include special purpose logic circuitry (e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit)). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question (e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them). The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing infrastructures, and grid computing infrastructures.

[0175] A computer program (also referred to as a program, software, software application, script, or code) may be written in any form of programming language (including compiled or interpreted languages, declarative languages, or procedural languages) and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or information / data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store portions of one or more modules, subroutines, or code). A computer program may be deployed to execute on one computer or multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

[0176] The processes and logic flows described herein can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input information / data and generating output. By way of example, processors suitable for executing computer programs include both general-purpose microprocessors and special-purpose microprocessors and any one or more processors of any type of digital computer. Generally speaking, the processor will receive instructions and information / data from a read-only memory or a random access memory or both. The basic elements of a computer are a processor for performing actions according to instructions and one or more memories for storing instructions and data. Generally speaking, a computer will also include one or more mass storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) for storing data, or be operably connected to the one or more mass storage devices to receive information / data from the one or more mass storage devices or to transfer information / data to the one or more mass storage devices, or both. However, a computer does not need to have such devices. Devices suitable for storing computer program instructions and information / data include all forms of non-volatile memory, media, and storage devices, including, by way of example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0177] To provide for interaction with a user, embodiments of the subject matter described herein may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information / data to the user, as well as a keyboard and pointing device (e.g., a mouse or trackball through which the user can provide input to the computer). Other types of devices may also be used to provide for interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including sound, voice, or tactile input. In addition, the computer may interact with the user by sending documents to and receiving documents from a device used by the user; for example, by sending a web page to a web browser in response to a request received from a web browser on the user's client device.

[0178] Embodiments of the subject matter described herein may be implemented in a computing system that includes a back-end component (e.g., as an information / data server), or includes a middleware component (e.g., an application server), or includes a front-end component (e.g., a client computer with a graphical user interface or web browser through which a user can interact with a specific implementation of the subject matter described herein), or any combination of one or more such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital information / data communication (e.g., a communication network). Examples of communication networks include local area networks ("LANs") and wide area networks ("WANs"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0179] The computing system may include a client and a server. The client and the server are generally remote from each other and typically interact via a communication network. The relationship between the client and the server is obtained by means of computer programs running on respective computers that have a client-server relationship with each other. In some embodiments, the server transmits information / data (e.g., an HTML page) to a client device (e.g., for displaying information / data to a user interacting with the client device and receiving user input from the user interacting with the client device). Information / data (e.g., results of user interactions) generated at the client device can be received at the server from the client device.

[0180] Although this specification includes many specific implementation details, these details should not be interpreted as limiting the scope of any disclosure or claimable content, but should be interpreted as descriptions of features that are specific to a particular disclosed embodiment. Certain features described herein in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a separate embodiment may also be implemented in multiple embodiments or in any suitable sub-combination. In addition, although features may be described above as working in certain combinations and even initially claimed as such, in some cases, one or more features from the claimed combination may be removed from the combination, and the claimed combination may be directed to a sub-combination or a variation of the sub-combination.

[0181] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that such operations be performed in the particular order shown or in a sequential order, or that all of the illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated together in a single software product or grouped into multiple software products.

[0182] Thus, certain embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

Claims

1. A method for monitoring the remaining useful life of an asset, comprising: receiving ( 602 ) system data associated with a target asset of an operational system, wherein operation of the target asset is affected by operation of an upstream asset, and operation of the target asset affects operation of a downstream asset; determining ( 604 ) at least one operational anomaly associated with the target asset based on the system data, wherein the operational anomaly indicates a deviation in operation of the target asset relative to standard operation; identifying ( 606 ) a root cause variable associated with the at least one operational anomaly; determining a first remaining life value for the root cause variable, the first remaining life value representing a time to reach a limit threshold associated with the root cause variable; determining a second remaining life value of an asset health index associated with the target asset that represents an overall operational health of the target asset based on the first remaining life value, the second remaining life value representing a remaining useful life of the target asset, wherein the asset health index associated with the target asset affects the operational health of at least one of an upstream asset and a downstream asset; At least one of a maintenance and a repair procedure of the target asset is initiated based on the first remaining life value and the second remaining life value. 2 . The method of claim 1 , wherein the asset health index represents the overall operational health of the target asset based on a combination of one or more root cause variables.

3. The method of claim 1 , wherein determining the first remaining lifetime value comprises: System data for the target asset is processed using a model, wherein the model is trained based on historical system data associated with the target asset and similar assets to identify the root cause variable.

4. The method according to claim 1, further comprising: At least one deviation between an expected trend of the at least one root cause variable and an actual trend of the at least one root cause variable is presented on a user interface.

5. The method according to claim 1, further comprising: A determination is made that the target asset is utilizing a selected mode from a plurality of configurable modes, and a second remaining life variable is determined based on the selected mode.

6. The method according to claim 1, wherein Initiating at least one of a maintenance and repair process for the target asset includes providing a prompt to perform maintenance on the target asset before the remaining useful life of the target asset expires.

7. The method of claim 1 , wherein providing the second remaining life value comprises presenting the second remaining life value, the method further comprising: receiving user input requesting display of the first remaining life value of the root cause variable; as well as The first remaining life value is presented.

8. The method according to claim 1, comprising: It is determined that the second remaining life value is below an operational threshold for initiating a maintenance notification.

9. An apparatus comprising at least one processor and at least one memory comprising computer program code, the at least one memory and the computer program code being configured to, with the at least one processor, cause the apparatus to perform any one of the methods according to claims 1 to 8.

10. A computer program product comprising at least one non-transitory computer-readable medium having computer-readable program instructions stored therein, the computer-readable program instructions comprising instructions that, when executed by a device, are configured to cause the device to perform at least any one of the methods according to claims 1 to 8.

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