Research on the composite fault diagnosis of gearbox based on local mean decomposition and Hilbert demodulation

Jingyue Wang, Tuojiang Chen, Jiangang Li, Haotian Wang, Junnian Wang
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Abstract

For the sake of deriving the signature frequencies of the composite malfunctions of broken and worn teeth of gearbox, the essay raised a method on account of local mean decomposition and Hilbert demodulation to diagnose gearbox composite fault. The local mean decomposition translates a complicated multi-component AM-FM signal into several PF elements with certain physical significance. Every PF component can be approximately regarded as a simplex component AM-FM signal. Using correlation coefficient method, the Hilbert envelope demodulation spectrum is analyzed by selecting the PF component which is strongly relevant to the incipient composite malfunction signal, and the malfunction signature frequency is identified from the demodulation spectrum. Through analyzing the emulation signal and the vibration signal of the actual gear box broken-wear complex fault, and compared with EMD, it is shown that the means can effectually discern the malfunction characteristic frequency in the composite fault signal.
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基于局部均值分解和Hilbert解调的齿轮箱复合故障诊断研究
为了得到齿轮箱断齿和磨损齿复合故障的特征频率,提出了一种基于局部均值分解和希尔伯特解调的齿轮箱复合故障诊断方法。局部均值分解将复杂的多分量AM-FM信号转化为若干个具有一定物理意义的PF元素。每个PF分量可以近似地看作一个单工分量AM-FM信号。采用相关系数法对希尔伯特包络解调谱进行分析,选取与初发复合故障信号密切相关的PF分量,从解调谱中识别故障特征频率。通过对实际齿轮箱断磨复合故障的仿真信号和振动信号的分析,并与EMD进行比较,表明该方法能有效地识别复合故障信号中的故障特征频率。
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