自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用

Adaptive Maximum Correlated Kurtosis Deconvolution Method and Its Application on Incipient Fault Diagnosis of Bearing

滚动轴承处于早期故障阶段时,特征信号微弱,并且受环境噪声影响严重,因此故障特征提取困难。针对这一问题,提出了基于自适应最大相关峭度解卷积的滚动轴承早期故障诊断方法。利用粒子群算法优良的寻优特性,并行搜寻最大相关峭度解卷积算法的影响参数,自适应地实现最佳的解卷积效果。故障信号通过影响参数优化的最大相关峭度解卷积算法处理后,冲击特性会得到增强,对解卷积信号做进一步包络解调分析,通过分析包络谱中幅值突出的频率成分可最终判定故障类型。仿真和实测信号分析结果表明,该方法可有效提取滚动轴承早期故障微弱特征频率信息。

The fault feature signal of rolling bearing is very weak and affected by environmental noise seriously in early failure period, so it is difficult to extract the fault feature. In order to solve this problem, an incipient fault diagnosis method for rolling bearing based on adaptive maximum correlated kurtosis deconvolution was proposed. Particle swarm optimization algorithm with excellent optimization characteristic was used to search for the influencing parameters of maximum correlated kurtosis deconvolution algorithm in order to achieve the best deconvolution result adaptively, then the impact characteristic of fault signal could be enhanced after processed by maximum correlated kurtosis deconvolution algorithm with optimized parameters. The envelope demodulation method was used to analyze the deconvolution signal further and the fault type could be judged by analyzing the obvious frequency components of the envelope spectrum. Analysis results of simulated and measured signal prove this method could extract the weak feature frequency information of incipient fault of rolling bearing effectively.