Multiscale Convolutional Neural Network With Self-Attention Mechanism and Soft Thresholding for Industrial Process Fault Diagnosis

Abstract

This article proposes a multiscale convolutional neural network (MSCNN)-based fault diagnosis method (AM-MAST) using a self-attention mechanism and soft thresholding. It aims to improve the accuracy of the industrial fault diagnosis model as well as the model’s immunity to interference. Unlike most current single-model fault diagnosis methods, this article employs an MSCNN and a multihead attention mechanism to extract features simultaneously. It also introduces an adaptive thresholding module at the end of the multiscale neural network to deal with interference noise. In addition, the features of the two networks are fused by a fusion module based on the attention mechanism; finally, the faults are accurately categorized by a classification module. This methodology effectively integrates both multiscale information and attention mechanisms, facilitating the proficient extraction of diverse features from the input dataset, while concurrently establishing interdependencies among distinct features. However, the incorporation of an adaptive soft threshold module efficiently mitigates noise interference. Experiments were conducted on the industrial coke furnace dataset, and it show that the method performs well and has a broad application prospect.