University of Oulu

Zhang, S., et al.: State of the art on vibration signal processing towards data-driven gear fault diagnosis. IET Collab. Intell. Manuf. 4( 4), 249– 266 (2022). https://doi.org/10.1049/cim2.12064

State of the art on vibration signal processing towards data-driven gear fault diagnosis

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Author: Zhang, Shouhua1,2; Zhou, Jiehan1; Wang, Erhua3;
Organizations: 1Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2School of Cyber Security and Computer, Hebei University, Baoding, China
3Changzhou College of Information Technology, Changzhou, China
4Beijing Aerospace Smart Manufacturing Technology Development Co., Ltd, Beijing, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 21.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2023060953083
Language: English
Published: John Wiley & Sons, 2022
Publish Date: 2023-06-09
Description:

Abstract

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

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Series: IET collaborative intelligent manufacturing
ISSN: 2516-8398
ISSN-E: 2516-8398
ISSN-L: 2516-8398
Volume: 4
Issue: 4
Pages: 249 - 266
DOI: 10.1049/cim2.12064
OADOI: https://oadoi.org/10.1049/cim2.12064
Type of Publication: A2 Review article in a scientific journal
Field of Science: 113 Computer and information sciences
Subjects:
Funding: The authors operate under Academy of Finland 6Genesis Flagship program (grants 318927) SRA4 Services and Applications.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2022 The Authors. IET Collaborative Intelligent Manufacturing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
  https://creativecommons.org/licenses/by-nc-nd/4.0/