Fault diagnosis of a rolling bearing based on the wavelet packet transform and a deep residual network with lightweight multi-branch structure

Abstract

Deep residual networks (DRNs) are a state-of-the-art deep learning model used in the data-driven fault diagnosis field. Their especially deep architectures give them sufficient capacity to deal with very complex diagnosis issues. However, a neural network with excellent performance usually requires hundreds of thousands of parameters, which is unaffordable for use in current industrial machines due to their limited computational resources. To enable practical applications for fault diagnosis, developing deep learning methods that can perform powerfully and have an economical computational burden is necessary. This study proposes a novel bearing fault diagnosis method based on the wavelet packet transform (WPT) and a lightweight variant of DRN called a multi-branch deep residual network (MB-DRN) in order to resolve the above issues. WPT is utilized to map raw signals into the time-frequency domain, from which the MB-DRN can extract a set of robust features more easily. Additionally, MB-DRN builds several small-sized convolutional layer branches in each building block to increase the network non-linearity, the construction of layer branches can be achieved freely and this design strategy largely saves the parameter usage while approaching a stronger model’s capacity. Two rolling bearing datasets with variable operating conditions were conducted on the proposed method to validate performance. The results verify the necessity of the WPT-based data processing method and show that MB-DRN can outperform the accuracies of standard DRN with only one quarter of the parameter amount, revealing the significant potential of the proposed method for realistic industrial fault diagnosis applications.