Bearing Fault Diagnosis under Variable Working Conditions Based on Deep Residual Shrinkage Networks and Transfer Learning

Xinyu Yang,Siyu Shao,Qiang Zhang,Fulin Chi,Haidong Shao

doi:10.1155/2021/5714240

Abstract

Nowadays, deep learning has made great achievements in the field of rotating machinery fault diagnosis. But in the practical engineering scenarios, when facing a large number of unlabeled data and variable operating conditions, only using a deep learning algorithm may reduce the performance. In order to solve the above problem, this paper uses a method of combining transfer learning with deep learning. First, the deep shrinkage residual network is constructed by adding soft thresholds to extract the characteristics of bearing vibration data under noise redundancy. Then, the joint maximum mean deviation (JMMD) criterion and conditional domain adversarial (CDA) learning domain adapting network are used to align the source and target domains. At the same time, adding transferable semantic augmentation (TSA) regular items improves alignment performance between classes. Finally, the proposed model is verified by three experiments: variable load, variable speed, and variable noise, which overcomes the shortcomings of traditional deep learning and shallow transfer learning algorithms.

Highlights

With the development of modern industry toward intelligence, the health management mode of industrial equipment based on big data has become a hot research field
The CWRU [30] bearing dataset is an open-source dataset of the Case Western Reserve University Laboratory which is widely used in the research of bearing fault diagnosis
It can be seen from the figure that in addition to the CORAL method, adopting other domain adaptation methods can greatly improve the accuracy of fault diagnosis under variable working conditions; especially when the Amplitude

Summary

Introduction

With the development of modern industry toward intelligence, the health management mode of industrial equipment based on big data has become a hot research field. To achieve the goal of real-time monitoring of mechanical health and performance, it is increasingly important to speed up the establishment of a stable and reliable Prognostic and Health Management (PHM) [1]. Since the measured signals are usually transient and dynamic, it is difficult to achieve early diagnosis of monitoring and failure by using the traditional time-frequency analysis method [2]. In order to ensure the highest possible uptime, the way of system maintenance should change to the way of real-time monitoring and predictive prevention [3]. To achieve these purposes, the intelligent fault diagnosis method has become an important research field in recent years

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Results

Conclusion