Review of Research on Rolling Bearing Health Intelligent Monitoring and Fault Diagnosis Mechanism

doi:10.3778/j.issn.1673-9418.2307005

Abstract

Abstract: As one of the most critical and failure-prone parts of the mechanical systems of industrial equipment, bearings are subjected to high loads for long periods of time. When they fail or wear irreversibly, they may cause accidents or even huge economic losses. Therefore, effective health monitoring and fault diagnosis are of great significance to ensure safe and stable operation of industrial equipment. In order to further promote the development of bearing health monitoring and fault diagnosis technology, the current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from the distribution of vibration signal data used, firstly, the relevant methods under uniform data distribution are sorted out, the classification, analysis and summary of the current research status are carried out mainly according to signal-based analysis and data-driven-based, and the shortcomings and defects of the fault detection methods in this case are outlined. Secondly, considering the problem of uneven data acquisition under actual working conditions, the detection methods for dealing with such cases are summarized, and different processing techniques for this problem in existing research are classified into data processing methods, feature extraction methods, and model improvement methods according to their different focuses, and the existing problems are analyzed and summarized. Finally, the challenges and future development directions of bearing fault detection in existing industrial equipment are summarized and prospected.

Key words: health monitoring, fault diagnosis, data distribution, signal analysis, data-driven

摘要： 轴承作为工业设备机械系统中最关键并且最容易发生故障的零件之一，长期处在高负荷的运行状态。当其发生故障时或者不可逆的磨损时，可能带来事故甚至造成巨大经济损失。因此，对其进行有效的健康监测和故障诊断，对于保障工业设备安全稳定运行有着重要的意义。为进一步促进轴承健康监测和故障诊断技术的发展，对当前现有的模型及方法进行分析与总结，并对现有技术进行划分、对比。从使用的振动信号数据分布出发，首先，对数据分布均匀下的相关方法进行整理，主要按照基于信号分析和基于数据驱动两方面进行研究现状的分类、分析与总结，对该情况下故障检测方法所存在的不足与缺陷进行概述。其次，考虑实际工况下数据采集通常具有不均衡特性的问题，对处理该类情况下的检测方法进行总结，并将现有研究中对该问题的不同处理技术根据其侧重点不同分为数据处理方法、特征提取方法、模型改进方法，并对所存在的问题进行分析。最后，对现有工业设备中轴承故障检测存在的挑战及未来发展方向进行了总结与展望。

关键词: 健康监测, 故障诊断, 数据分布, 信号分析, 数据驱动

WANG Jing, XU Zhiwei, LIU Wenjing, WANG Yongsheng, LIU Limin. Review of Research on Rolling Bearing Health Intelligent Monitoring and Fault Diagnosis Mechanism[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(4): 878-898.

王婧, 许志伟, 刘文静, 王永生, 刘利民. 滚动轴承健康智能监测和故障诊断机制研究综述[J]. 计算机科学与探索, 2024, 18(4): 878-898.

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