A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis
A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis
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DOI：
10.1115/1.4000770
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发表时间：
2010-04-01
影响因子：
1.7
通讯作者：
Zi, Yanyang
中科院分区：
文献类型：
--
作者：
Wang, Yanxue;He, Zhengjia;Zi, Yanyang
旋转机械的健康诊断可以在早期发现潜在的故障，并减少严重的机器损坏和代价高昂的机器停机。近年来，自适应分解方法由于在实际应用中受人类操作者影响较小，引起了许多研究人员的关注。本文通过数值模拟从局部均值、分解分量、瞬时频率和类小波滤波特性四个方面比较了局部均值分解（LMD）和经验模态分解（EMD）两种自适应方法。比较结果表明，LMD 比 EMD 可以获得更准确的瞬时频率和更有意义的信号解释。然后将 LMD 和 EMD 分别用于两台实际工业旋转机械的摩擦冲击故障和蒸汽激发振动故障的健康诊断。结果表明，LMD 似乎比 EMD 更适合早期故障检测，并且具有更好的性能。由此证明LMD有潜力成为旋转机械监测和诊断的有力工具。 [DOI：10.1115/1.4000770]
Health diagnosis of the rotating machinery can identify potential failure at its early stage and reduce severe machine damage and costly machine downtime. In recent years, the adaptive decomposition methods have attracted many researchers' attention, due to less influences of human operators in the practical application. This paper compares two adaptive methods: local mean decomposition (LMD) and empirical mode decomposition (EMD) from four aspects, i.e., local mean, decomposed components, instantaneous frequency, and the waveletlike filtering characteristic through numerical simulation. The comparative results manifest that more accurate instantaneous frequency and more meaningful interpretation of the signals can be acquired by LMD than by EMD. Then LMD and EMD are both exploited in the health diagnosis of two actual industrial rotating machines with rub-impact and steam-excited vibration faults, respectively. The results reveal that LMD seems to be more suitable and have better performance than EMD for the incipient fault detection. LMD is thus proved to have potential to become a powerful tool for the surveillance and diagnosis of rotating machinery. [DOI: 10.1115/1.4000770]