An improved MSCNN and GRU model for rolling bearing fault diagnosis

Wang, Teng (author), Tang, Youfu (author), Wang, Tao (author), Lei, Na (author)

In this paper, a novel fault diagnosis method based on the fusion of squeeze and excitation-multiscale convolutional neural networks (SENet-MSCNN) and gate recurrent unit (GRU) is proposed to address the problem of low diagnosis rate caused by the fact that normal samples are much larger than fault samples in the vibration big data. The method takes the time-domain vibration signal as input and fuses the spatial features extracted by SENet-MSCNN. The temporal features extracted by GRU in order to bring them into the fully connected layer for identification so as to realize the intelligent diagnosis of rolling bearing adaptive feature extraction. Finally, the method is applied to the simulated signal and experimental data for testing and analysis. The results reveal that the model can reach 98.98 % and 76.44 % migration diagnostic accuracy in bearing and gearbox datasets. At the same time, it has strong noise immunity, adaptivity, and robustness, providing an effective way for intelligent diagnosis of rolling bearing vibration big data.

• Language: English
• Keywords: SENet, multiscale convolutional neural networks, gate recurrent unit, rolling bearings, fault diagnosis
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: Str. 261-274
• Numbering: Vol. 69, no. 5/6
• PID: 20.500.12556/RUL-146703
• UDC: 621.8
• ISSN on article: 0039-2480
• DOI: 10.5545/sv-jme.2022.459
• COBISS.SI-ID: 154984707
• Publication date in RUL: 08.06.2023

Record is a part of a journal

• Title: Strojniški vestnik
• Shortened title: Stroj. vestn.
• Publisher: Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
• ISSN: 0039-2480
• COBISS.SI-ID: 762116

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Title: Diagnosticiranje napak kotalnih ležajev na podlagi izboljšanih modelov MSCNN in NRU
• Keywords: SENet, MSCNN, GRU, kotalni ležaji, diagnosticiranje napak

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: Youth Science Foundation of Northeast Petroleum University
• Project number: 2018QNL-28