Back to Search Start Over

Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning

Authors :
Du Zhehua
Lin Xin
Source :
E3S Web of Conferences, Vol 257, p 02030 (2021)
Publication Year :
2021
Publisher :
EDP Sciences, 2021.

Abstract

In modern production, the precision and the importance of rotating machinery is higher and higher in the direction of large-scale, high speed and automation development, so that the traditional fault diagnosis methods are insufficient to deal with massive, multi-source and high-dimensional data, cannot meet the requirements of security and reliability. Therefore, several typical deep learning models are briefly introduced at first and the application of deep learning in fault diagnosis of rotor system, gear box and rolling bearing in recent years is studied and analyzed based on its strong feature extraction ability and advantages of clustering analysis. Finally, the advantages and disadvantages of deep learning model are summarized and the fault diagnosis methods of rotating machinery are summarized and prospected based on engineering practice.

Subjects

Subjects :
Environmental sciences
GE1-350

Details

Language :
English, French
ISSN :
22671242
Volume :
257
Database :
Directory of Open Access Journals
Journal :
E3S Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.88987275109a4804b590d501edf466b7
Document Type :
article
Full Text :
https://doi.org/10.1051/e3sconf/202125702030