Wind Turbine Gearbox Fault Diagnosis using SAE-BP Transfer Neural Network

doi:10.23940/ijpe.19.09.p24.25042514

Abstract

Abstract: The gearbox is a key component in wind turbines, and the fault diagnosis of gearboxes in wind turbines is a significant process of reliability management. Therefore, a SAE-BP transfer neural network is proposed in this paper for fault diagnosis of gearboxes in wind turbines. The proposed method is conducted by two processes. Firstly, a source task data is served as the training process to pretrain the SAE-BP neural network. The final learned network structure is the transferable weights or parameters that contain the feature information. Then, the learned weights are transferred into a target task with different working and fault conditions as the initial weight of a neural network model. To extract more fault-sensitive features, fast Fourier transform (FFT) is introduced to transform the raw data into a frequency domain. Several comparison experiments are conducted to validate the proposed method, and the results show that the proposed method achieves higher classification accuracy.

Key words: intelligent fault diagnosis, gearbox, transfer learning, sparse autoencoder, BP algorithm

Yu Wang, Shuai Yang, and René Vinicio Sánchez. Wind Turbine Gearbox Fault Diagnosis using SAE-BP Transfer Neural Network [J]. Int J Performability Eng, 2019, 15(9): 2504-2514.

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