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An analysis method of gear fault diagnosis based on Chirp-Z transform and local mean decomposition

机译:基于Chirp-Z变换和局部均值分解的齿轮故障诊断分析方法

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A combined fault diagnosis method based on the CZT (Chirp-Z transform) and LMD (Local mean decomposition) marginal spectrum is proposed in consideration of the complex nonlinearities and non-stationary properties presented by the fault vibration signal in the gear. In this method, the original vibration signal is initially decomposed through LMD into several stationary PF (product function) components with physical meaning, and then the PF components that contain main fault information are selected for CZT local zoom analysis. The LMD marginal spectrum is determined by calculation of instantaneous amplitude and frequency information of each PF to characterize the overall distribution of fault signals. The results of the experiment research on the simulated signals and the gear crack faults indicate that with high fault recognition rate, the combined fault diagnosis method can be adopted to reflect the characteristics in variation and distribution of fault signals both from local and overall perspectives.
机译:提出了一种基于CZT(Chirp-Z变换)和LMD(局部均值分解)边际谱的组合故障诊断方法,该方法考虑了齿轮故障振动信号所呈现的复杂非线性和非平稳特性。在这种方法中,最初的振动信号首先通过LMD分解为具有物理意义的几个固定PF(乘积函数)分量,然后选择包含主要故障信息的PF分量进行CZT局部缩放分析。 LMD边际频谱是通过计算每个PF的瞬时幅度和频率信息确定的,以表征故障信号的整体分布。仿真信号和齿轮裂纹故障的实验研究结果表明,故障识别率高,可以采用组合故障诊断方法从局部和整体的角度反映故障信号变化和分布的特征。

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