A Novel Wind Turbine Health Condition Monitoring Method Based on Correlative Features Domain Adaptation

Abstract

Aimed at the difficulty in fault diagnosis of wind turbine transmission system under variable working conditions, the paper proposes a novel health condition monitoring method based on correlative features domain adaptation. Firstly, the envelope analysis of the collected signals is carried out, and the time–frequency features of the signals are extracted to construct the feature set. The feature sets under the similar working conditions to target are selected as the auxiliary sample sets in source domain through the transferability evaluation. Then, a transformation matrix is found to adapt the marginal and conditional distributions of wind turbine sample data under different working conditions, and its weight is adjusted. While reducing the discrepancy between domains, the class imbalance problem is taken into consideration, so as to improve the accuracy of fault diagnosis under the target working condition. Finally, the classifier is trained with the adjusted source domain and tested in the target domain. Experiments show that the proposed method can effectively improve the accuracy of wind turbine fault diagnosis.