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JP-7857086-B2 - Methods and computing systems for performing predictive health analysis on assets

JP7857086B2JP 7857086 B2JP7857086 B2JP 7857086B2JP-7857086-B2

Inventors

  • パベル・スタノ
  • フランク・キルシュニック

Assignees

  • ヒタチ・エナジー・リミテッド

Dates

Publication Date
20260512
Application Date
20210531
Priority Date
20200608

Claims (15)

  1. A method for performing predictive health analysis on assets (11-13; 16-18) of power system assets or industrial assets , wherein the method is: This includes the step of performing multiple independent probabilistic simulations using the transition probabilities of a discrete Markov chain model, The discrete Markov chain model has a state space that includes a set of asset health states (41-44), Each of the aforementioned independent probabilistic simulations simulates the future evolution of the state space of the discrete Markov chain model over the forecast period. A different set of transition probabilities between the asset health states (41-44) is used for one of the independent probabilistic simulations run in parallel, the different set of transition probabilities is associated with different operating and/or ambient conditions, and the method further, The step includes calculating the development (60;80) of the predicted asset health state over the forecast period from the aforementioned independent probabilistic simulations, The step of calculating the development of the predicted asset health state includes calculating the probability distribution (61-64) in the state space multiple times within the prediction period, and mapping the probability distribution to a scalar representing deterioration. method .
  2. The method according to claim 1, wherein the step of calculating the development of the predicted asset health state includes the step of calculating RUL(60;80).
  3. The method according to claim 1 or 2 , further comprising the step of calculating confidence information or variance information (81, 82) regarding the development of the predicted asset health state as a function of time over the forecast period.
  4. The method according to claim 3, wherein the confidence information includes the future development of the confidence interval over the forecast period, or the variance information includes the future development of the variance over the forecast period.
  5. The method according to claim 3 or 4, wherein the confidence information or variance information includes a lower time evolution and an upper time evolution, the lower limit is associated with a first set of transition probabilities, and the upper limit is associated with a second set of transition probabilities different from the first set of transition probabilities.
  6. The aforementioned state space is At least one state (41) in which the operation of the asset is not adversely affected by a malfunction, There is at least one state (42, 43) in which the operation of the asset is adversely affected by a malfunction, but the asset continues to operate. The method according to any one of claims 1 to 5, further comprising (44) a state in which the asset is not functioning due to a malfunction.
  7. The method according to any one of claims 1 to 6, wherein the development of the predicted asset health state is obtained as the time evolution of the scalar.
  8. The method according to any one of claims 1 to 7, further comprising the step of determining the transition probabilities used for simulation among the independent probabilistic simulations from historical data including sensor data for a plurality of assets, the sensor data being labeled with a fault signature indicating what state in the state space each asset was in at what point in time.
  9. The method according to any one of claims 1 to 8, wherein the aforementioned independent probabilistic simulations are Markov chain Monte Carlo (MCMC) simulations.
  10. The method according to any one of claims 1 to 9, wherein the discrete Markov chain model is homogeneous and each state (41 to 44) in the state space has a non-zero transition probability to at most one other state in the state space.
  11. The steps include receiving sensor measurement data from different groups of sensors captured during the operation of the asset, The method further includes the step of updating the development of the predicted asset health state by determining the transition probability used in one of the independent probabilistic simulations from each group of received sensor measurement data, The sensor measurement data received from the different groups described above indicates the different operating conditions and/or ambient conditions. The method according to any one of claims 1 to 10, wherein the sensor measurement data is labeled with a fault signature indicating what state in the state space each asset was in at what point in time .
  12. The method according to any one of claims 1 to 11, wherein the asset is a power transformer, a distributed energy resource DER unit, or a generator, and/or the forecast period is one year or more, two years or more, three years or more, four years or more, five years or more, ten years or more, fifteen years or more, or twenty years or more.
  13. A method for operating and/or maintaining assets (11-13; 16-18) of power system assets or industrial assets, wherein the method is: A step of performing a predictive asset health analysis for the asset using the method described in any one of claims 1 to 12, A method comprising the step of automatically performing at least one of the following: generating an alarm or warning based on the calculated predicted asset health state development; generating a control signal for the control operation of the asset based on the calculated predicted asset health state development; scheduling downtime for the asset based on the calculated predicted asset health state development; scheduling maintenance work based on the calculated predicted asset health state development; scheduling replacement work based on the calculated predicted asset health state development; and changing the maintenance interval based on the calculated predicted asset health state development.
  14. A computing system (20-24; 100) that functions to perform predictive health analysis on power system assets or industrial assets , wherein the computing system comprises at least one integrated circuit, The at least one integrated circuit functions to perform multiple independent probabilistic simulations using the transition probabilities of a discrete Markov chain model. The discrete Markov chain model has a state space that includes a set of asset health states (41-44), Each of the aforementioned independent probabilistic simulations simulates the future evolution of the state space of the discrete Markov chain model over the forecast period. A different set of transition probabilities between the asset health states (41-44) is used for one of the independent probabilistic simulations run in parallel, the different set of transition probabilities is associated with different operating and/or ambient conditions, and the integrated circuit further, The system functions to calculate the development of the predicted asset health state over the forecast period from the aforementioned independent probabilistic simulations . A computing system for calculating the development of the predicted asset health state, comprising the steps of calculating the probability distributions (61-64) in the state space multiple times within the prediction period, and mapping the probability distributions to scalars representing deterioration .
  15. Industrial or power systems (10; 15), Assets (11-13; 16-18), An industrial or power system comprising a computing system (20-24; 100) according to claim 14, which performs predictive asset health analysis on the aforementioned assets (11-13; 16-18), wherein the computing system is optionally a distributed controller of the industrial or power system for controlling the aforementioned assets .

Description

Field of Invention This invention relates to a technology for evaluating the soundness of assets. In particular, this invention relates to a method and apparatus for predictive evaluation of asset soundness. Background of the Invention Power systems, such as power generation, transmission, and/or distribution systems, as well as industrial systems, include assets. Transformers, generators, and distributed energy resource (DER) units are examples of such assets. These assets degrade during operation. For planning purposes, and for scheduling maintenance or replacement work, it is desirable to estimate the remaining useful life (RUL) of the assets. RUL (Reliable Asset Value) estimation can be performed based on sensor data from a group of assets of the same or similar type as the asset for which RUL estimation is being performed. Labeling the sensor data with fault signatures can indicate whether the data corresponds to the asset's normal functioning state, degraded state, or faulty state. Combining such sensor data for RUL estimation can be challenging, as different types of sensor data may be available for various assets within a group, depending on the manufacturer or the auxiliary sensors installed on the asset. Y. Yu et al., “Remaining Useful Life Prediction Using Elliptical Basis Function Network and Markov Chain”, World Academy of Science, Engineering and Technology, 47, 2010, describes a method for predicting remaining useful life using an elliptic basis function (EBF) network and a Markov chain. To account for missing covariates, the EBF structure is trained using a modified expectation-maximization (EM) algorithm. The Markov chain is constructed to represent the evolution of the external covariates, but explicit extrapolation to the internal covariates is not required. This is a schematic diagram of a power system having a computing system according to a certain embodiment.This is a schematic diagram of a power system having a computing system according to a certain embodiment.This figure shows the Markov chain model used in the embodiment.This is a flowchart illustrating a method according to a certain embodiment.This is a graph showing a specific example of output generated by a method and computing system according to a certain embodiment.This is a bar graph illustrating the time evolution of state occupancy probabilities.This is a graph showing a specific example of output generated by a method and computing system according to a certain embodiment.This graph shows the results of the predicted asset health analysis combined with the observed asset health status.This graph shows the results of the predicted asset health analysis combined with the observed asset health status.This is a flowchart illustrating a method according to a certain embodiment.This is a block diagram of a computing system according to a certain embodiment. Detailed Description of Embodiments Embodiments of the present invention will be described with reference to drawings in which the same or similar reference numerals indicate the same or similar components. Some embodiments will be described in the context of assets in a power system, such as a distributed energy resource (DER) unit or a transformer, but the embodiments are not limited thereto. Features of the embodiments can be combined with each other unless otherwise specified. Figures 1 and 2 are schematic diagrams of power systems 10 and 15. Power systems 10 and 15 include multiple assets. These assets may include generators, transformers, or other power system assets such as distributed energy resource (DER) units 11-13 and 16-18. The power systems 10 and 15 include a control system, which includes local controllers 21 to 23, each associated with an asset. The control system may include a central system 20. The central system 20 may be communicatively coupled to the local controllers. The central system 20 may also be communicatively coupled to a remote (e.g., cloud-based) server system 24. As will be described in more detail below, the local controllers 21-23, the central system 20, and/or the remote server system 24 may function to perform predictive asset health analysis using a Markov chain model. The Markov chain model may have a specific configuration, as will be described below. The local controllers 21-23, the central system 20, and/or the remote server system 24 may function to perform predictive asset health analysis by running multiple independent probabilistic simulations, particularly Markov chain Monte Carlo (MCMC) simulations. The results of the predictive asset health analysis can be used by the local controllers 21-23, the central system 20, and/or the remote server system 24 to schedule downtime, maintenance, and replacement work, or to automatically perform control operations. The local controllers 21-23, the central system 20, and/or the remote server system 24 may function to generate and output control or output data. The output may be provided