Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing
Abstract Wind farm operators traditionally rely on SCADA temperature alarms for early signs of main bearing degradation. However, these alarms are sometimes delayed due to slow propagation of temperature in the main bearing. This study proposes the use of turbine multi-sensor vibration data as a via...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00409-3 |
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| author | Samuel M. Gbashi Obafemi O. Olatunji Paul A. Adedeji Nkosinathi Madushele |
| author_facet | Samuel M. Gbashi Obafemi O. Olatunji Paul A. Adedeji Nkosinathi Madushele |
| author_sort | Samuel M. Gbashi |
| collection | DOAJ |
| description | Abstract Wind farm operators traditionally rely on SCADA temperature alarms for early signs of main bearing degradation. However, these alarms are sometimes delayed due to slow propagation of temperature in the main bearing. This study proposes the use of turbine multi-sensor vibration data as a viable alternative. Anomaly detection models including One-class support vector machine (OCSVM) and isolation forest (IF) models are first employed to compute anomaly scores from the dataset. An exponentially weighted moving average (EWMA) control chart receives the anomaly scores for fault prognosis. The study developed a methodology for identifying the optimal contamination fraction of the anomaly detection models and an index called the “anomaly model evaluation index (AMEI)” for evaluating the performance of the anomaly detection models. The optimal contamination fraction for the anomaly detection models was 4%. The IF model outperformed the OCSVM model, with an AMEI index of 5.84, in contrast to the OCSVM model’s score of 5.32. However, the OCSVM computed 18 times faster than the IF model. Furthermore, EWMA-IF achieved a higher True Positive Rate of 79.8% compared to 59.94% for EWMA-OCSVM, indicating a better ability to correctly identify abnormal observations. The EWMA-IF model alerted for an approaching main bearing fault six hours earlier than the EWMA-OCSVM control chart. The persistence of the anomaly scores above the threshold of the control charts provides evidence to suggest that a potential main bearing failure is imminent. |
| format | Article |
| id | doaj-art-ba3af3a056d14af1a1944d5fcaa860fa |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-ba3af3a056d14af1a1944d5fcaa860fa2025-08-20T03:05:10ZengSpringerDiscover Artificial Intelligence2731-08092025-07-015112310.1007/s44163-025-00409-3Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearingSamuel M. Gbashi0Obafemi O. Olatunji1Paul A. Adedeji2Nkosinathi Madushele3Department of Mechanical Engineering Science, University of JohannesburgDepartment of Mechanical Engineering Science, University of JohannesburgDepartment of Mechanical Engineering Science, University of JohannesburgDepartment of Mechanical Engineering Science, University of JohannesburgAbstract Wind farm operators traditionally rely on SCADA temperature alarms for early signs of main bearing degradation. However, these alarms are sometimes delayed due to slow propagation of temperature in the main bearing. This study proposes the use of turbine multi-sensor vibration data as a viable alternative. Anomaly detection models including One-class support vector machine (OCSVM) and isolation forest (IF) models are first employed to compute anomaly scores from the dataset. An exponentially weighted moving average (EWMA) control chart receives the anomaly scores for fault prognosis. The study developed a methodology for identifying the optimal contamination fraction of the anomaly detection models and an index called the “anomaly model evaluation index (AMEI)” for evaluating the performance of the anomaly detection models. The optimal contamination fraction for the anomaly detection models was 4%. The IF model outperformed the OCSVM model, with an AMEI index of 5.84, in contrast to the OCSVM model’s score of 5.32. However, the OCSVM computed 18 times faster than the IF model. Furthermore, EWMA-IF achieved a higher True Positive Rate of 79.8% compared to 59.94% for EWMA-OCSVM, indicating a better ability to correctly identify abnormal observations. The EWMA-IF model alerted for an approaching main bearing fault six hours earlier than the EWMA-OCSVM control chart. The persistence of the anomaly scores above the threshold of the control charts provides evidence to suggest that a potential main bearing failure is imminent.https://doi.org/10.1007/s44163-025-00409-3Anomaly detectionExponentially weighted moving average control chartFault prognosisIsolation forestOne-class support vector machineVibration |
| spellingShingle | Samuel M. Gbashi Obafemi O. Olatunji Paul A. Adedeji Nkosinathi Madushele Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing Discover Artificial Intelligence Anomaly detection Exponentially weighted moving average control chart Fault prognosis Isolation forest One-class support vector machine Vibration |
| title | Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing |
| title_full | Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing |
| title_fullStr | Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing |
| title_full_unstemmed | Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing |
| title_short | Control chart-integrated machine learning models for incipient fault detection in wind turbine main bearing |
| title_sort | control chart integrated machine learning models for incipient fault detection in wind turbine main bearing |
| topic | Anomaly detection Exponentially weighted moving average control chart Fault prognosis Isolation forest One-class support vector machine Vibration |
| url | https://doi.org/10.1007/s44163-025-00409-3 |
| work_keys_str_mv | AT samuelmgbashi controlchartintegratedmachinelearningmodelsforincipientfaultdetectioninwindturbinemainbearing AT obafemioolatunji controlchartintegratedmachinelearningmodelsforincipientfaultdetectioninwindturbinemainbearing AT paulaadedeji controlchartintegratedmachinelearningmodelsforincipientfaultdetectioninwindturbinemainbearing AT nkosinathimadushele controlchartintegratedmachinelearningmodelsforincipientfaultdetectioninwindturbinemainbearing |