信号分解及其在机械故障诊断中的应用研究综述

doi:10.3901/JME.2020.17.091

摘要/Abstract

摘要： 重大装备制造业是国民经济的支柱，也是关系到国家安全的战略性产业，而重大机械装备的运行安全一直是备受关注的焦点。机械设备由于工作环境恶劣、工况复杂，其关键部件容易受损，导致设备性能退化，甚至造成设备崩溃。健康状态监测和故障诊断是保证重大机械装备安全运行的必要手段。通过信号分解可以抑制机械振动信号中的环境噪声和无关成分干扰，从而有效提取故障特征，因此信号分解在机械故障诊断中发挥着关键作用。目前，围绕信号分解理论及其在机械故障诊断中的应用，国内外学者开展了大量研究工作。首先，从时域、频域和时频域三个方面系统综述了国内外学者对信号分解理论的研究现状；其次，从轴承、齿轮和转子碰摩三个方面详细梳理了信号分解在机械故障诊断中的应用研究现状；最后，归纳总结了信号分解及其在机械故障诊断应用中面临的挑战。

关键词: 状态监测, 故障诊断, 信号分解, 轴承故障, 转子碰摩

Abstract: Major equipment manufacturing industry is the pillar of national economy and is also the strategic industry related to the national security. The operation safety of major equipment has always been the focus of public attention. Due to the harsh working conditions, the key components of the mechanical equipment are prone to failure, which will result in performance degradation and even breakdown of the equipment. Condition monitoring and fault diagnosis are necessary to ensure the safe operation of the major equipment. Signal decomposition techniques can suppress the interferences of noise and other unconcern ed signal components, and thus can effectively extract fault features from the vibration signal. Therefore, signal decomposition plays a key role in machine fault diagnosis. At present, researchers have carried out extensive research on signal decomposition th eory and its applications in machine fault diagnosis. Firstly, the advances of research on signal decomposition theory are reviewed from the time-domain, frequency-domain and time-frequency domain methods; then, the advances of research on the applications of the signal decomposition in machine fault diagnosis are also reviewed from the bearing, gear and rotor rub-impact fault diagnosis; finally, the challenges facing the field are summarized.

Key words: condition monitoring, fault diagnosis, signal decomposition, bearing fault, rotor rub-impact

中图分类号:

TH12
TB535

陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述[J]. 机械工程学报, 2020, 56(17): 91-107.

CHEN Shiqian, PENG Zhike, ZHOU Peng. Review of Signal Decomposition Theory and Its Applications in Machine Fault Diagnosis[J]. Journal of Mechanical Engineering, 2020, 56(17): 91-107.

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