基于谱峭度法和自适应遗传算法的轴承故障诊断

Rolling Bearings Fault Diagnosis Based on Adaptive Genetic Algorithm and Spectral Kurtosis Algorithms

结合谱峭度法和自适应遗传算法,提出滚动轴承故障诊断的一种新方法。将谱峭度法给出的滤波器参数作为初始参数,再利用自适应遗传算法对其优化得到最优参数,最后进行包络分析,诊断出故障。

This paper presents a new method for rolling bearings fault diagnosis using the combination of spectral kurtosis （SK） algorithms and adaptive genetic algorithm （AGA）. First, the initial filter parameters are taken from SK algorithms that give the best SK. Then the optimal parameters of the band - pass filter are obtained using AGA to optimize the initial filter parameters, and diagnose the fault with envelope analysis finally. The feasibility and effectiveness of this algorithm proposed are demonstrated by experiments of rolling bearings fault diagnosis.