基于改进最大相关峭度解卷积的滚动轴承复合故障诊断

强背景噪声环境下,多故障特征的准确分离是滚动轴承复合故障诊断的关键与难点。针对此问题,提出了一种改进最大相关峭度解卷积的滚动轴承复合故障诊断方法。该方法基于故障信号的特点,利用最大相关峭度解卷积实现信号中的多故障特征分离,借助改进的粒子群算法对参数进行优化选取;利用互相关谱进一步突出信号中的故障特征,提高信噪比。仿真信号和实测滚动轴承内、外圈复合故障信号的分析表明,所提方法能够准确提取出滚动轴承复合故障特征,借助互相关谱的噪声抑制能力,能实现比单一MCKD方法更为有效的故障特征提取。

Compound Fault Diagnosis of Rolling Bearings Based on Improved Maximum Correlated Kurtosis Deconvolution

The accurate separation of multiple fault features is the key and difficult point for compound fault diagnosis of rolling bearings under strong background noise.Hence,a compound fault diagnosis method is proposed based on the improved maximum correlated kurtosis deconvolution(MCKD).Based on the characteristic of fault signals,multiple fault features are separated by MCKD and the parameters are optimized by the improved Particle Swarm Optimization algorithm.The cross-correlation spectrum is employed to further enhance the fault features of the signal,and the signal-to-noise ratio is improved.Through analysis of simulation signals and measured compound fault signals of inner and outer rings of rolling bearings,the results show that the compound fault features of rolling bearings can be extracted accurately by the proposed method,the noise components can be further suppressed due to the use of cross-correlation spectrum and the fault features can be extracted more effectively than the MCKD method.