Systems and methods for variable power generation asset failure prediction

Patent metadata

Field	Value
Publication number	US-2025284272-A1
Application number	US-202519074280-A
Country	US
Kind code	A1
Filing date	Mar 7, 2025
Priority date	Mar 7, 2024
Publication date	Sep 11, 2025
Grant date	—

Abstract

An example method includes receiving monitoring data from at least one variable power generation asset, predicting, using a trained machine model, a predicted operational stressor value based on features of the monitoring data, the operational stressor value being a stressor metric of an operational stressor, comparing a difference of the predicted operational stressor value to a reported operational stressor value to create a residual value, retrieving a particular anomaly threshold from a plurality of anomaly thresholds, the particular anomaly threshold being selected based on at least one manufacturing variable of a component and a risk category that is based on an operational survivability analysis over time for that at least one manufacturing variable in a presence of the operational stressor, the at least one manufacturing variable being from manufacturing data, and providing a notification of potential failure when the residual value is outside the particular anomaly threshold.

First claim

1 . A non-transitory computer-readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising:
receiving monitoring data from at least one variable power generation asset; predicting, using a trained machine model, a predicted operational stressor value based on features of the monitoring data, the operational stressor value being a stressor metric of an operational stressor of a condition of at least a part of the at least one variable power generation asset; comparing a difference of the predicted operational stressor value to a reported operational stressor value to create a residual value, the reported operational stressor value having been being based on a measurement of the reported operational stressor value of the at least one variable power generation asset; retrieving a particular anomaly threshold from a plurality of anomaly thresholds, the particular anomaly threshold being selected based on at least one manufacturing variable of a component of the at least one variable power generation asset and a risk category that is based on an operational survivability analysis over time for that at least one manufacturing variable in a presence of the operational stressor, the at least one manufacturing variable being from manufacturing data; and providing a notification of potential failure when the residual value is outside the particular anomaly threshold.

2 . The non-transitory computer-readable medium of claim 1 , further comprising:
receiving the manufacturing data that includes manufacturing variables for different components of a plurality of variable power generation assets including the at least one manufacturing variable, each manufacturing variable of the manufacturing data including a plurality of dimensional metrics; for each manufacturing variable:
sorting the plurality of dimensional metrics based on size;
performing a manufacturing survivability analysis on different percentile ranges of the sorted dimensional metrics to determine manufacturing risk categories based at least in part on risk of failure over time; and
performing the operational survivability analysis on at least a subset of risk categories to determine operational risk categories based on risk of failure over time in the presence of at least one operational risk variable.

3 . The non-transitory computer-readable medium of claim 2 , wherein performing the manufacturing survivability analysis on the different percentile ranges of the sorted dimensional metrics to determine the manufacturing risk categories based at least in part on the risk of failure over time comprises determining at least one first p-value to determine significance of the different percentile ranges of the sorted dimensional metrics, at least one manufacturing risk category being determined based, at least in part, on the first p-value being sufficiently low.

4 . The non-transitory computer-readable medium of claim 2 , wherein performing the operational survivability analysis on at least the subset of risk categories to determine the operational risk categories based on the risk of failure over time in the presence of at least one operational risk variable comprises determining at least one second p-value to determine significance of the different operational risk categories, at least one operational risk category being determined based, at least in part, on the second p-value being sufficiently low.

5 . The non-transitory computer-readable medium of claim 1 , the method further comprising:
identifying different anomaly thresholds for different operational risk categories; and storing the different anomaly thresholds within the plurality of anomaly thresholds.

6 . The non-transitory computer-readable medium of claim 1 , wherein the predicted operational stressor is a predicted bearing temperature and the reported operational stressor value is a reported bearing temperature.

7 . The non-transitory computer-readable medium of claim 1 , wherein the predicted operational stressor is a predicted bearing vibration and the reported operational stressor value is a reported bearing vibration.

8 . The non-transitory computer-readable medium of claim 1 , wherein the manufacturing variable is selected from a housing drill diameter, a spacer width, or an axial clearance reduction after assembly.

9 . The non-transitory computer-readable medium of claim 1 , wherein the features are lagged bearing temperature and lagged and current active power

10 . The non-transitory computer-readable medium of claim 1 , wherein the at least one variable power generation asset is a wind turbine.

11 . The non-transitory computer-readable medium of claim 1 , wherein the manufacturing variables for different components of the manufacturing data are related to gearbox components.

12 . The non-transitory computer-readable medium of claim 1 , wherein the model is one of a plurality of models, each model of the plurality of models being for a different variable power generation asset.

13 . The non-transitory computer-readable medium of claim 1 , wherein the model includes an XGBoost based learning model.

14 . A system, comprising:
at least one processor; and memory, the memory containing executable instructions, the executable instructions being executable by the at least one processor to: receive monitoring data from at least one variable power generation asset; predict, using a trained machine model, a predicted operational stressor value based on features of the monitoring data, the operational stressor value being a stressor metric of an operational stressor of a condition of at least a part of the at least one variable power generation asset; compare a difference of the predicted operational stressor value to a reported operational stressor value to create a residual value, the reported operational stressor value having been being based on a measurement of the reported operational stressor value of the at least one variable power generation asset; retrieve a particular anomaly threshold from a plurality of anomaly thresholds, the particular anomaly threshold being selected based on at least one manufacturing variable of a component of the at least one variable power generation asset and a risk category that is based on an operational survivability analysis over time for that at least one manufacturing variable in the presence of the operational stressor, the at least one manufacturing variable being from manufacturing data; and provide a notification of potential failure when the residual value is outside the particular anomaly threshold.

15 . The system of claim 14 , wherein the executable instructions are further executable by the at least one processor to:
receive the manufacturing data that includes manufacturing variables for different components of a plurality of variable power generation assets including the at least one manufacturing variable, each manufacturing variable of the manufacturing data including a plurality of dimensional metrics; for each manufacturing variable:
sort the plurality of dimensional metrics based on size;
perform a manufacturing survivability analysis on different percentile ranges of the sorted dimensional metrics to determine manufacturing risk categories based at least in part on risk of failure over time; and
perform the operational survivability analysis on at least a subset of risk categories to determine operational risk categories based on risk of failure over time in the presence of at least one operational risk variable.

Assignees

• Utopus Insights Inc

Inventors

• Keerthisinghe Chanaka
• Fluchel Kim
• Guthrie Douglas
• Theis Thomas N
• Machado Da Silva Ana Rita Reis
• Yu Xueyin

Classifications

• F03D17/0065 Primary

  for diagnostics · CPC title

• G01M13/045

  Acoustic or vibration analysis · CPC title

• G05B23/027

  Alarm generation, e.g. communication protocol; Forms of alarm · CPC title

• G05B23/024 Primary

  Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks · CPC title

• G05B13/041

  in which a variable is automatically adjusted to optimise the performance · CPC title