Abstract:  Due to the characteristics of strong noise, nonlinearity, and non-stationarity, the vibration signal of rolling bearing was difficult to extract and its working condition was difficult to identify. Therefore, a rolling bearing fault recognition model based on improved ensemble multiple hidden layers wavelet ELM network (IEMHLWEN) was proposed. Firstly, a new spectral segmentation method was proposed and the collected rolling bearing vibration signals were decomposed by spectral segmentation wavelet transform, and the decomposed components which could better reflect the characteristics of bearing conditions were selected and reconstructed. Finally, different multiple hidden layers wavelet ELM networks were designed by employing different wavelet functions, and the reconstructed signals were fed into different deep networks for automatic feature learning and fault identification. The final result was obtained by ensemble learning method. The experimental results show that the average fault identification accuracy of proposed method reaches 99.42% and the standard deviation is only 0.11. The ability of condition automatic feature extraction and automatic condition identification are better than deep learning methods such as deep sparse autoencoder, deep denoising autoencoder, deep belief network and so on, and it is suitable for automatic identification of rolling bearing faults.

Key words:  rolling bearing; ensemble learning; fault identification; extreme learning machine; wavelet transform;improved ensemble multiple hidden layers wavelet ELM network(IEMHLWEN)

ZHAO Fan-chao, DAI Shi-liang, FANG Hua-wei, et al. Fault identification of rolling bearing based on improved ensemble multiple hidden layers wavelet ELM network［J］.Journal of Mechanical & Electrical Engineering, 2021,38(9):1152-1159.