Demodulation spectrum analysis for multi-fault diagnosis of rolling bearing via chirplet path pursuit

Abstract

The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features, making it difficult to identify the fault characteristic frequency. To remove the time-varying behavior caused by speed fluctuation, the phase function of target component is necessary. However, the frequency components induced by different faults interfere with each other. More importantly, the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation. As such, we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit. First, the envelope signal is obtained by applying Hilbert transform to the raw signal. Second, the characteristic frequency is extracted via chirplet path pursuit, and the other underlying components are calculated by the characteristic coefficient. Then, the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency. Next, the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component. Finally, the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies. The method is validated by simulated and experimental signals.

摘要

由于非平稳工况下多故障轴承复杂调制特征的影响, 使得识别故障特征频率十分困难。为了消 除振动信号时变工况影响, 必须获得特征频率的相位函数。然而, 多故障的特征频率之间互相干扰, 影响了瞬时频的提取。因此, 本文提出一种基于线调频小波路径追踪的多故障轴承改进解调频谱分析 方法。对原始信号进行Hilbert 变换得到包络信号;使用线调频小波路径追踪算法从包络信号提取特 征频率, 并且根据特征系数计算其他特征频率;根据振动信号的时变特征计算各成分的能量因子;迭 代使用能量因子可调广义解调算法和带通滤波器获得解调信号;对解调信号进行频谱分析, 识别轴承 特征频率。仿真和试验信号的分析验证了该算法的有效性。