A Research of a Modified Bearing Fault Diagnosis Model Based on Deformable Atrous Convolution and Squeeze-and-Excitation Aggregation

Abstract

In order to accurately and intelligently identify the fault signal characteristics of bearing in modern rotating machinery industries, a comprehensive novel fault diagnosis model is introduced by using a few data points as input signals. In this paper, the main methods of the model are processing the two signal inputs, frequency-domain signal (FDS) and time-frequency graph (TFG), into a network built up with convolution (Conv) and deformable atrous Conv. Then, this network extracts their features respectively. After squeezing-and-excitation aggregation on the features, three types of outputs are obtained for tasks of faulty bearing position detection, fault type diagnosis, and damage size estimation. This method allows the fault diagnosis of different locations, fault types, and fault degrees to be completed simultaneously, and the state-of-Art effect can be achieved. Two bearing vibration signal datasets are used in the paper to evaluate the performance of the model, and experimental results prove an effective multi-task fault diagnosis ability on the three tasks.