基于MCKD和增强倒频谱的直升机自动倾斜器滚动轴承故障诊断方法

针对直升机自动倾斜器滚动轴承转速低，其早期故障特征信号易被噪声淹没的问题，提出了基于最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution , 简称MCKD）和增强倒频谱分析的直升机自动倾斜器滚动轴承故障诊断方法。该方法首先利用MCKD方法对故障信号进行降噪处理，然后采用自相关方法和广义Shannon熵对倒频谱分析进行改进，得到增强倒频谱，提取故障特征频率。实验研究表明，所提出的方法能精确地诊断自动倾斜器滚动轴承内圈、外圈和滚珠故障，且优于传统的倒频谱分析，可以预防重大故障发生。
Abstract：Aiming at extracting the early fault features of helicopter swash-plate rolling bearings,usually submerged easily by noises due to its low rolling speed,a fault diagnosis method based on the maximum correlated kurtosis deconvolution (MCKD) and enhanced cepstrum was proposed.First,the fault signal was de-noised by the MCKD method.Then,an enhanced cepstrum analysis method was formed by introducing the self-correlation method and generalized Shannon entropic method.The fault characteristic frequencies were extracted from the enhanced cepstrum.The experiments results show that the proposed method can be used to diagnose bearing faults of the inner race,outer race and balls accurately.It is more effective compared with the traditional cepstrum analysis.The method can be used for the preventive monitoring of these kinds of bearings．