基于QGA和SK的滚动轴承故障诊断新方法  

Rolling bearing fault diagnosis based on quantum genetic algorithm and spectral kurtosis

作　　者：徐可君[1] 任帅[2] 秦海勤[1] 李志农[2] 

机构地区：[1]海军航空工程学院青岛校区航空机械系,青岛266041 [2]南昌航空大学测试与光电工程学院,南昌330063

出　　处：《振动与冲击》2015年第11期105-109,共5页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(50775208;51075372)

摘　　要：基于量子遗传算法收敛速度快、全局寻优能力强的特点,将谱峭度法和量子遗传算法相融合,提出一种滚动轴承故障诊断的新方法。该方法以经初始带通滤波后的原信号的谱峭度作为量子遗传算法的适应度函数,重新优化设计最优滤波器,以设计的最优带通滤波器对原始信号滤波并进行包络分析,从而实现轴承故障的诊断。实测数据分析表明,方法所得峭度谱值明显增大,包络谱线更加干净,故障特征频率更加明显。同时研究发现,方法诊断精度几乎不受传感器位置的影响。Based on rapid convergence and good global search capacity,a new method for rolling bearing fault diagnosis was presented by combining quantum genetic algorithm（QGA）and spectral kurtosis（SK）.The original signal＇s SK gained by the primary band-pass filtering was taken as the fitness function of QGA to redesign the optimal band-pass filter.Then the original signal was filtered with the optimal band-pass filter redesigned.The characteristic frequencies of rolling bearing faults could be nicely obtained with the envelope analysis.The method was used to analyze actual testing signals.The results showed that the SK values obtained with the new method increase obviously,the envelope spectral lines of the filtered signals and the characteristic frequencies of faults are much clearer than those of the traditional SK results;the accuracy of the new method is not dependent upon the positions of sensors.

关 键 词：滚动轴承 量子遗传算法 谱峭度 包络分析 故障诊断 

分 类 号：TH133[机械工程—机械制造及自动化]