
基于局部均值分解的边际谱在滚动轴承故障诊断中的应用
Application of marginal spectrum based on local mean decomposition in rolling bearing fault diagnosis
Local mean decomposition(LMD)can decompose complex multi-component signal into a linear combination of a finite set of product functions(PFs), and after obtaining the instantaneous amplitudes and instantaneous frequencies of all PF components the marginal spectrum based on LMD can be calculated. Aiming at the big end-point error problem of the direct instantaneous frequency extraction method, an improved direct method was put forward. The new rolling bearing fault diagnosis method named marginal spectrum based on LMD was proposed, and was applied in actual rolling bearing fault diagnosis. The analysis results show that the fault characteristic frequency can be extracted effectively, and the fault position can be determined.
局部均值分解 / 边际谱 / 滚动轴承 / 故障诊断 / 瞬时频率 {{custom_keyword}} /
local mean decomposition / marginal spectrum / rolling bearing / fault diagnosis / instantaneous frequency {{custom_keyword}} /
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