A novel bearing intelligent fault diagnosis method based on spectrum sparse deep deconvolution

The extraction of fault-induced repetitive transients which possess cyclo-stationarity is the key to the fault diagnosis of rotating machinery, which is of considerable significance for ensuring the safe and reliable operation of machinery equipment. Traditional deconvolution methods mainly aim to recover fault-related impulsive features from the time domain and are prone to give poor fault diagnosis results under heavy interference conditions. To solve this problem, a spectrum sparse deep deconvolution method (SSDD) with a deep neural network structure is proposed in this paper. The proposed method uses an envelope spectrum sparse criterion as the cost function to seek an optimal inverse filter through a deep neural network. Firstly, a special band-averaging strategy is designed to initialize the filters in the input layer of the neural network with a window method to provide a direction for deconvolution. Secondly, envelope spectral kurtosis that can depict the sparse feature in the envelope spectrum domain is taken as the cost function to guide the training of the deep network and lock the fault information. Then, the optimal weights are realized by the eigenvalue algorithm, and the weak sparse features are enhanced and extracted layer by layer. Finally, the most significant fault information is obtained through dimension reduction. The simulated and experimental data analysis results verified that the proposed method is superior to traditional deconvolution methods in fault diagnosis performance and robustness to random impulses and strong background noise.