Investigation of Deep Transfer Learning for Cross-Turbine Diagnosis of Wind Turbine Faults


 Data-driven fault diagnosis of wind turbines has gained popularity and accordingly various deep learning models have been developed with massive data and achieved excellent diagnosis performance. However, most existing deep learning models require the similar distribution in both training and testing data, thus the trained model cannot generalize the new wind turbines with different data distribution. Also, there lacks enough fault data in practice and therefore the cost of training a new model from scratch is extremely high. To solve these problems, a cross-turbine fault diagnosis method based on deep transfer learning is proposed for wind turbines with the available supervisory control and data acquisition (SCADA) data. To better capture the spatial features of SCADA data, a deep multi-scale residual attention convolutional neural network (DMRACNN) is first designed. Then, distribution differences between source and target domain data are aligned at feature level. Specifically, we investigate the transfer performance of four different domain adaption metrics. We evaluate our proposed method using SCADA data from two wind turbines to compare the diagnostic performance of four basic networks combined with four transfer metrics. Compared with traditional deep learning methods, our proposed DMRACNN achieved significant performance improvements. A cross-validation experiment within two turbines demonstrates the strong generalization ability of the proposed method.