Fault Detection of Wind Turbine Bearing Based on BO-SDAE Multi-source Signal

doi:10.16182/j.issn1004731x.joss.20-0045

Abstract

Abstract: Due to the discrepancy within signals from sensors of wind turbines caused by environmental interference, the fault detection results of wind turbine bearing will be affected and the multi-source signal fault diagnosis method is proposed to improve the reliability of fault detection. The time-domain and frequency-domain features of bearing vibration signals, noise signals and temperature signals are used for feature extraction,and then the features are transmitted to the stacked denoising autoencoders, which are optimized the hidden layer node structure by the Bayesian optimization algorithm to achieve multi-source signal feature fusion. Softmax function is used for classification. Experiments show that the accuracy of this method is higher than that of the single signal fault diagnosis method, and still maintains a high accuracy rate with mixed speed as experimental data.

Key words: wind turbine, multi-source signal, stacked denoising autoencoders, Bayesian optimization, fault diagnosis

CLC Number:

TP391.9

Wu Dinghui, Zhu Zhichao, Han Xinhong. Fault Detection of Wind Turbine Bearing Based on BO-SDAE Multi-source Signal[J]. Journal of System Simulation, 2021, 33(5): 1148-1156.

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