RMCW: An Improved Residual Network With Multi-Channel Weighting for Machinery Fault Diagnosis

Abstract

Faced with increasingly complex industrial data, standard machine learning algorithms struggle to effectively extract both linear and nonlinear features. In this study, an improved residual network (ResNet) called Residual network with Independent Multi-Channel Weighting (RMCW) to tackle the nonlinear, temporally uncertain, and unevenly distributed fault. Firstly, a strategy for constructing the multi-channel vibration intrinsic mode function (IMF) images is designed to obtain the primary features by combing the empirical mode decomposition (EMD) and the gramian angular field (GAF). Secondly, a dynamic receptive field (DRF) with independent channel weighting is utilized to adaptively fuse the multi-channel features. This renders both initialization parameters for each individual channel and DRF parameters mutually independently adaptive to the fault features in the different batch. Thirdly, the RMCW model is built by inputting the fused features to the network of 9 residual building blocks. Two experimental cases verify that the propose method is effective for the machinery fault diagnosis and is superior to the comparing methods.

Keywords

• Feature fusion
• fault diagnosis
• deep learning
• vibration signal