Deep Transfer Learning Models for Industrial Fault Diagnosis Using Vibration and Acoustic Sensors Data: A Review

Abstract

In order to evaluate final quality, nondestructive testing techniques for finding bearing flaws have grown in favor. The precision of image processing-based vision-based technology has greatly improved for defect identification, inspection, and classification. Deep Transfer Learning (DTL), a kind of machine learning, combines the superiority of Transfer Learning (TL) for knowledge transfer with the benefits of Deep Learning (DL) for feature representation. As a result, the discipline of Intelligent Fault Diagnosis has extensively developed and researched DTL approaches. They can improve the robustness, reliability, and usefulness of DL-based fault diagnosis techniques (IFD). IFD has been the subject of several thorough and excellent studies, although most of them have appraised important research from an algorithmic standpoint, neglecting real-world applications. DTL-based IFD strategies have also not yet undergone a full evaluation. It is necessary and imperative to go through the relevant DTL-based IFD publications in light of this. Readers will be able to grasp the most cutting-edge concepts and develop practical solutions to any IFD challenges they may encounter by doing this. The theory behind DTL is briefly discussed before describing how transfer learning algorithms may be included into deep learning models. This research study looks at a number of vision-based methods for defect detection and identification utilizing vibration acoustic sensor data. The goal of this review is to assess where vision inspection system research is right now. In this respect, image processing as well as deep learning, machine learning, transfer learning, few-shot learning, and light-weight approach and its selection were explored. This review addresses the creation of defect classifiers and vision-based fault detection systems.