基于MOMEDA与CS自适应随机共振的滚动轴承微弱故障特征提取

ROLLING BEARING WEAK FAULT FEATURE EXTRACTION BASED ON MULTIPOINT OPTIMAL MINIMUM ENTROY DECONVOLUTION ADJUSTED AND ADAPTIVE STOCHASTIC RESONANCE WITH CUCKOO SEARCH

针对调节非线性系统参数的取值会影响输出信噪比(SNR)的大小这一现象,采用信噪比(SNR)作为随机共振输出评价指标,提出将多点优化最小熵解卷积调整(Multipoint Optimal Minimum Entroy Deconvolution Adjusted,MOMEDA)与布谷鸟自适应随机共振相结合的方法来提取微弱故障特征频率,仿真分析表明将MOMEDA作为随机共振前处理能够显著提升故障微弱信号,而实验实例验证进一步表明将MOMEDA方法与随机共振相结合能有效地从存在强噪声的信号中提取弱故障信号的特征频率,从而实现滚动轴承弱故障的诊断。

Adjusting the value of nonlinear system parameters will affect the output Signal-to-Noise Ratio(SNR),so Signalto-Noise Ratio(SNR)was used as the output evaluation index of stochastic resonance.A method combining Multipoint Optimal Minimum Entroy Deconvolution Adjusted(MOMEDA)with Cuckoo search adaptive Stochastic Resonance(SR)was proposed to extract weak fault feature frequencies.Simulation analysis shows that using MOMEDA as the stochastic resonance pretreatment can significantly improve the weak fault signal,and experimental example verification further shows that the combination of MOMEDA method and stochastic resonance can effectively extract the characteristic frequency of weak fault signal from the signal with strong noise,so as to realize the weak fault diagnosis of rolling bearings.