Compound fault separation and diagnosis method for online rolling bearings based on RSEUnet and 1DCNN

Abstract

Compound fault signals interfere with one another, resulting in an inconspicuous feature extraction that requires sophisticated signal processing techniques and expert experience. However, good online diagnostic methods are not available to carry out this process. This paper proposes a method based on Residual Connection and Squeeze-and-Excitation Unet (RSEUnet) and one-dimensional convolutional neural network (1DCNN). The process includes fault separation and diagnosis. First, the feature extraction module of the RSEUnet network introduces an attention mechanism and a residual connection that adaptively assigns various weights to different channels. This model is used to train the maps of the fault signal after time-frequency transformation. Ideal binary masks with excellent performance are the training targets to complete the intelligent separation of compound faults. Second, the 1DCNN is used as a feature learning model to efficiently learn the features of single faults from time-domain signals. An embedded system consisting of a Jetson Nano and a signal acquisition circuit is then built to perform online diagnosis. The test is carried out on the fault experimental platform. Results show that the method has an accuracy of 99.71%, making it highly suitable for the diagnosis of bearing compound faults.