A comprehensive review of deep learning-based fault diagnosis approaches for rolling bearings: Advancements and challenges
Source abstract
Abstract
Rolling bearing fault diagnosis is an important technology for health monitoring and pre-maintenance of mechanical equipment, which is of great significance for improving equipment operation reliability and reducing maintenance costs. This article reviews the research progress of fault diagnosis methods for rolling bearings, with a focus on analyzing the applications, advantages, and disadvantages of traditional data-driven methods, deep learning methods, graph embedding methods, and Transformer methods in this field. In addition, further analysis was conducted on the main issues of current research, including complex network structures, insufficient information attention, difficulties in graph data processing, and challenges in long-term dependency modeling. In response to these challenges, future research should focus on designing more lightweight and efficient models, improving computational efficiency, robustness of the models, and strengthening attention and deep mining of fault features.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Experimental Training Teaching Management Department, West Anhui University 1 , Lu’an 237012,
College of Electrical and Information Engineering, Anhui University of Technology 2 , Maanshan 243002,
Funding
National Natural Science Foundation of China · Horizontal Class II Scientific Research Project of West Anhui University · Natural Science Key Project of West Anhui University · Anhui Province University Outstanding Young Talent Support Program Project
References (100+)
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