Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis

Abstract

The rolling element bearing is an important component in most rotating machinery. The unexpected failure of a bearing may cause the whole mechanism to break down. Hence, research has focused on developing effective intelligent fault diagnosis to generate more accurate and robust diagnostic results. Bearing fault diagnosis based on stacked sparse autoencoder (SSAE) architecture is proposed in this study. SSAE is capable of providing a featureless methodology for bearing fault diagnosis. However, the architecture of SSAE is greatly influenced by its hyperparameter settings and there is no standard method of determining the optimal hyperparameter values. In addition, the standard learning algorithm used in SSAE architecture is time-intensive. In this paper, a method that combines differential evolution and a resilient back-propagation approach is proposed to improve the performance of SSAE networks in bearing fault classification. The differential evolution approach optimised SSAEs hyperparameters such as the hidden nodes number, weight decay parameter, sparsity parameter, and weight of the sparsity penalty term, that are associated with each hidden layer of SSAE networks. An increase in the hidden layers of SSAE will further complicate the hyperparameter selection process. The resilient back-propagation training algorithm is used to train the SSAE network due to its low computation cost. Results from analysis of three databases demonstrate that the proposed model achieved 99% performance accuracy in bearing fault diagnosis. The proposed model is found to be more user-friendly and effective in handling multi-condition of bearing faults compared to the original autoencoder.