Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

•Wavelet auto-encoder is designed with wavelet function to capture the signal characteristics.•A deep wavelet auto-encoder is constructed with multiple wavelet auto-encoders to enhance the unsupervised feature learning ability.•The proposed method effectively diagnoses the different fault types, different fault severities and different fault orientations of rolling bearing. Unsupervised feature learning from the raw vibration data is a great challenge for rolling bearing intelligent fault diagnosis. In this paper, a novel method called deep wavelet auto-encoder (DWAE) with extreme learning machine (ELM) is proposed for intelligent fault diagnosis of rolling bearing. Firstly, wavelet function is employed as the nonlinear activation function to design wavelet auto-encoder (WAE), which can effectively capture the signal characteristics. Secondly, a DWAE is constructed with multiple WAEs to enhance the unsupervised feature learning ability. Finally, ELM is adopted as the classifier to accurately identify different bearing faults. The proposed method is applied to analyze the experimental bearing vibration signals, and the results confirm that the proposed method is superior to the traditional methods and standard deep learning methods.