Adaptive Fault Diagnosis of Rolling Bearings based on Crest Factor of Envelope Spectrum
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摘要: 表征滚动轴承故障特征的周期性冲击，特别是在故障早期阶段，常常被噪声和其它结构振动所淹没，从而难以辨别。共振解调被广泛用于滚动轴承故障冲击特征提取，但其滤波频带的参数选择常需要一定的先验知识。针对现有的频带优化方法的不足，本文提出一种基于包络谱谱峰因子和复平移Morlet小波滤波的自适应共振解调方法-自适应包络谱谱峰因子算法。包络谱谱峰因子（Crest factor of envelope spectrum，CE）定义为包络谱在一定范围内的最大值和有效值之比，能有效度量信号中周期性冲击强弱，结合粒子群优化算法的寻优特性，对Morlet小波滤波器中心频率和带宽参数进行优化。将包络谱谱峰因子作为适应度函数来比较不同参数组合下的滤波效果，根据适应度函数值最大原则选取Morlet小波滤波器参数。仿真信号、实验信号以及工程实际信号分析验证了该方法在共振解调最优频带选取中的有效性和优越性。Abstract: Local defects like pitting, spalling and indentation in rolling bearings, manifest themselves as repetitive impulses in collected vibrations. Such impulses along with their repetitive frequency could be considered as bearing fault features. However, it is challenging to identify them due to noise and the interfering vibrations from other components, especially at the early stage of faults. Resonance demodulation (RD) is a known method to extract bearing fault features, but the central frequency and bandwidth of its band-pass filter need to be determined in advance. A new paradigm is proposed in the present study to bring the RD technique into full play. At first, we defined a novel indicator to measure the fitness of a band-pass filter, which is the ratio of the maximum to the root mean square value of the envelop spectrum of the filtered signal, called herein the crest of envelop spectrum (CE). Secondly, complex shifted Morlet wavelet was chosen to construct a band-pass filter considering its wave similarity with the impulses rendered by local defects in bearings. At last, the paradigm makes use of particle swarm optimization (PSO) to find the optimal central frequency and bandwidth of Morlet wavelet using CE as the fitness function. Simulated, experimental and field signals verify the effectiveness and advantages of the proposed RD paradigm.
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