Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (7): 51-63.DOI: 10.3778/j.issn.1002-8331.2208-0079

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Acoustic Signal-Based Industrial Equipment Fault Diagnosis

ZHOU Yurong, ZHANG Qiaoling, YU Guangzeng, XU Weiqiang   

  1. 1.School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
    2.School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
    3.College of Textile Science and Engineering(International Institute of Silk), Zhejiang Sci-Tech University, Hangzhou 310018, China
  • Online:2023-04-01 Published:2023-04-01



  1. 1.浙江理工大学 信息科学与工程学院,杭州 310018
    2.浙江理工大学 计算机科学与技术学院,杭州 310018
    3.浙江理工大学 纺织科学与工程学院(国际丝绸学院),杭州 310018

Abstract: In order to guarantee the safety and stability of the industrial production process, it is of great significance and value to adopt reasonable fault diagnosis. Thus, fault diagnosis of industrial equipment has always been a hotspot in the field of industrial control. Firstly, this paper discusses the significance of fault diagnosis, and points out the feasibility and advantages of fault diagnosis based on acoustic signal. Then, according to whether the deep learning is involved, acoustic signal-based fault diagnosis approaches are segmented into traditional-based and deep learning-based categories. Then, it combs the essential ideas and flow of two categories respectively, expounds and summarizes the principle, advantages, limitations, main methods and diagnostic results. Finally, the paper points out the research difficulties, hotspots and the future development direction in the area of industrial equipment fault diagnosis.

Key words: acoustic signal, fault diagnosis, industrial equipment, machine learning, deep learning

摘要: 为了保证工业生产过程的安全稳定运行,采取合理的故障诊断具有十分重要的意义和价值。因此,工业设备故障诊断一直是工业领域的研究热点。阐述了故障诊断的意义,并指出基于声信号进行故障诊断的可行性和优势。根据有无深度学习的参与,将基于声信号的故障诊断方法分为基于传统和基于深度学习两种类型;分别梳理了两类故障诊断方法的流程与思路,阐述并归纳了两类方法中典型算法的原理、优点、局限性、主要方法及诊断效果。最后,指出了当前工业设备故障诊断领域的研究难点、热点以及未来发展方向。

关键词: 声信号, 故障诊断, 工业设备, 机器学习, 深度学习