基于混沌与模糊聚类的机械故障自动识别

Automatic Diagnosis Techniques of Machinery Fault Based on Chaos and Fuzzy Clustering Analysis

针对大型设备旋转部件故障模式复杂难以识别的特点,给出一种基于混沌与模糊最大似然估计(Fuzzy maximum likelihood estimates,FMLE)聚类相结合的机械故障自动识别方法。利用混沌振子在非平衡相变对小信号非常敏感,而对噪声和高频信号具有强免疫力的特点,可检测出微弱的周期故障特征信号的频率信息,并将其作为故障特征矢量输入模糊聚类分类器进行聚类分析。同时针对传统的模糊C均值(Fuzzy center means,FCM)聚类算法只适用于球形或者类球形数集分布的缺陷,将基于最大似然估计的距离测度引入故障特征聚类中,实现对不同形状、大小和密度的故障数据集模糊聚类,达到对机械故障自动识别的效果。试验及工程实例结果证明了方法的有效性,同时证明FMLE聚类具有更好的聚类效果。

Aiming at the difficulty of recognizing fault pattern of rotating parts in mechanical equipment,a new method for fault diagnosis based on chaos and fuzzy maximum likelihood estimates（FMLE） clustering algorithm is introduced.The non-equilibrium phase change of chaos oscillator is very sensitive to small signal and immune against the random noise and the high frequency signal.The frequency of the weak fault signals is extracted easily,which can be used to cluster analysis as fault feature vectors.Considering that the traditional fuzzy c-means（FCM） clustering algorithm is only suited to spherical-shape distribution dataset,distance norm based on the fuzzy maximum likelihood estimates is introduced,which suits to datasets with different shape and size,density and the different faults in rotating machinery are detected automatically.Results of experimental and engineering test indicated that the method is effective,and the cluster effect based on FMLE clustering is better.