基于最大相关峭度反褶积的轴承故障诊断方法

针对滚动轴承的故障信号是周期性冲击信号这一特性，提出了最大相关峭度反褶积(maximum correlated kurtosis deconvolution，简称MCKD)与谱峭度(spectral kurtosis，简称SK)结合的滚动轴承早期故障诊断方法,即MCKD-SK法。利用MCKD方法可以有效提取滚动轴承早期故障信号中被噪声淹没的周期冲击成分，抑制信号中的噪声，实现信号降噪，提升原信号的峭度。利用SK方法可以选择合理频带，将信号中的低频信息从高频信息中解调出来。通过仿真与实际监测数据的分析和验证，证明MCKD-SK方法可以准确有效地诊断滚动轴承的早期故障，可用于滚动轴承早期故障的在线监测。

Bearing Fault Diagnosis Based on Maximum Correlated Kurtosis Deconvolution

According to the view that the rolling bearing fault signal is a periodic pulsing signal, a method based on maximum correlated kurtosis deconvolution (MCKD) and spectral kurtosis (SK) called the MCKD-SK method is proposed in order to diagnose early faults of rolling bearing. This method can effectively suppress noise in the signal in order to extract the bearing early fault signal from the signal submerged by noise and improve the kurtosis of the original signal. The SK method is then used to select a reasonable frequency band and demodulate the low frequency information from the high frequency band. Through simulation and actual monitoring, as well as data analysis and validation, the MCKD-SK method has being shown to accurately and effectively diagnose early faults of rolling bearing. This method is also suitable for on line monitoring early motor bearing faults.