Fault Diagnosis Framework of Rolling Bearing Using Adaptive Sparse Contrative Auto-Encoder with Optimized Unsupervised Extreme Learning Machine

Abstract

Nowadays, the intelligent fault diagnosis based on deep learning have achieved remarkable results in the fields of the industrial equipment health monitoring and management. To implement the adaptive feature extraction and fault isolation for key components of rotating machinery (rolling bearings, etc.), two new algorithms, Adaptive Sparse Contrative Auto-encoder (ASCAE) algorithm and Optimized Unsupervised Extreme Learning Machine (OUSELM) classifier by Cuckoo Search Algorithm (CSA), can be firstly designed in this paper, respectively. Furthermore, a new rolling bearing fault diagnosis framework based on ASCAE combined with OUSELM is first of all proposed in this paper. Accordingly, this designed fault diagnosis framework can be divided into three main steps: i). Firstly, the vibration signals of rolling bearings can be collected and processed on the key components of rotating machinery, and then the collected vibration signals can be accordingly converted into frequency signals; ii). Secondly, the transformed spectral signals can be entered into the constructed ASCAE for feature learning to exploit the multi-layer sensitive features from the hidden raw data; iii). Thirdly, the extracted multi-layer sensitive features can be flowed into the trained OUSELM classifier for unsupervised fault state separation and diagnosis. More specifically, our designed fault diagnosis framework (ASCAE-OUSELM) can employ the homotopy regularization theory, sparse theory, intelligent optimization algorithm and other tools to optimize the parameters and improve the performance of the original Contrative Auto-encoder (CAE) algorithm and Unsupervised Extreme Learning Machine (USLEM) algorithm, respectively. At the same time, the proposed fault diagnosis framework can achieve effective sparse and sensitive feature information extraction in the feature extraction stage (ASCAE) to avoid over-fitting. In fault isolation stage, the issue of the supervised and low training efficiency caused by traditional deep learning model can be perfectly addressed by OUSELM. Eventually, the experimental data of rolling bearings validated the effectiveness of the proposed fault diagnosis framework and two deigned algorithms.