A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine

Weiyu Wang, Xunxin Zhao, Lijun Luo, Pei Zhang, Fan Mo, Fei Chen, Diyi Chen, Fengjiao Wu and Bin Wang
Additional contact information
Weiyu Wang: Wuling Power Corporation Ltd., Changsha 410004, China
Xunxin Zhao: Wuling Power Corporation Ltd., Changsha 410004, China
Lijun Luo: Wuling Power Corporation Ltd., Changsha 410004, China
Pei Zhang: Wuling Power Corporation Ltd., Changsha 410004, China
Fan Mo: Wuling Power Corporation Ltd., Changsha 410004, China
Fei Chen: Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
Diyi Chen: Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
Fengjiao Wu: Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
Bin Wang: Department of Power and Electrical Engineering, Northwest A&F University, Xianyang 712100, China

Energies, 2022, vol. 15, issue 22, 1-19

Abstract: To address the difficulty of early fault diagnosis of rolling bearings, this paper proposes a rolling bearing diagnosis method by combining the attention entropy and adaptive deep kernel extreme learning machine (ADKELM). Firstly, the wavelet threshold denoising method is employed to eliminate the noise in the vibration signal. Then, the empirical wavelet transform (EWT) is utilized to decompose the denoised signal, and extract the attention entropy of the intrinsic mode function (IMF) as the feature vector. Next, the hyperparameters of the deep kernel extreme learning machine (DKELM) are optimized using the marine predators algorithm (MPA), so as to achieve the adaptive changes in the DKELM parameters. By analyzing the fault diagnosis performances of the ADKELM model with different kernel functions and hidden layers, the optimal ADKELM model is determined. Compared with conventional machine learning models such as extreme learning machine (ELM), back propagation neural network (BPNN) and probabilistic neural network (PNN), the high efficiency of the method proposed in this paper is verified.

Keywords: rolling bearing; fault diagnosis; empirical wavelet transform; attention entropy; marine predators algorithm; deep kernel extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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