Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection
In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wi...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Chinese Automatic Control Society
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/74497 |
| _version_ | 1848763291515486208 |
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| author | Habibi, Hamed Howard, Ian Habibi, R. |
| author_facet | Habibi, Hamed Howard, Ian Habibi, R. |
| author_sort | Habibi, Hamed |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one-step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated. |
| first_indexed | 2025-11-14T11:01:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-74497 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:01:08Z |
| publishDate | 2018 |
| publisher | Chinese Automatic Control Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-744972020-07-27T02:39:48Z Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection Habibi, Hamed Howard, Ian Habibi, R. In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the fault probability is calculated. The proposed algorithm is implemented on a wind turbine benchmark model to detect drivetrain sensor faults, which are one of the most addressed and likely faults in offshore wind turbines. The fault probability estimation effectively eliminates the need for installing identical redundant sensors. Indeed, because of the use of the unknown wind speed estimator, the residual signal, constructed based on the drivetrain estimated states, is not able to clearly signify the fault periods, a situation in which the fault probability accurately does this task. Also, using the proposed algorithm, the fault size for each sensor is estimated via a one-step calculation, which decreases the complexity of this algorithm. The fault identification is performed using the recursive least square method and two other modifications, including exponentially weighted and windowed estimates. Additionally, in the fault accommodation step, the concept of a virtual sensor is used to remove the need for reconfiguring the current controller, which reduces complexity and expense. In the simulation section, using a real measured wind speed for two different fault scenarios, the proposed algorithm is evaluated and finally, conclusions are stated. 2018 Journal Article http://hdl.handle.net/20.500.11937/74497 10.1002/asjc.1973 Chinese Automatic Control Society restricted |
| spellingShingle | Habibi, Hamed Howard, Ian Habibi, R. Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection |
| title | Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection |
| title_full | Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection |
| title_fullStr | Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection |
| title_full_unstemmed | Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection |
| title_short | Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection |
| title_sort | bayesian fault probability estimation: application in wind turbine drivetrain sensor fault detection |
| url | http://hdl.handle.net/20.500.11937/74497 |