Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning

To take full advantage of the limited monitoring data with fault information for operational state prediction in the case of the discrepancy in data distribution between the WTGs, a novel combined method is proposed based on the long short-term memory, fuzzy synthesis and feature-based transfer learning. After the statistical analysis and prediction of the monitoring indexes of two 2-MW WTGs with faulty information, an operational state calibration framework is proposed based on deep learning and fuzzy synthesis. Following this, three feature-based transfer learning methods are adopted to narrow the discrepancy among the data distribution of the WTGs. Correspondingly, feasibility verification of the proposed method is equally addressed. Case applications are performed using the actual monitoring data from No. 13 and 15 wind turbines of a wind farm in northern China. The results show that the operational state calibration framework can sensitively detect the potential fault information of the WTG in advance. Meanwhile, three transfer learning algorithms can effectively narrow the distance of the data distribution among the WTGs, and the classification accuracy can almost reach above 0.9. The proposed method can make full use of existing monitoring data with faulty information to predict the status of other WTGs.(c) 2022 Published by Elsevier Ltd.

Automated summary

This study proposes a novel method for operational state prediction of wind turbine generators (WTGs) using limited monitoring data and fault information. The proposed method combines long short-term memory, fuzzy synthesis, and feature-based transfer learning to address the discrepancy in data distribution among the WTGs. Experimental results demonstrate that the proposed method can sensitively detect potential faults in advance and achieve high accuracy.