基于威布尔与模糊C均值的滚动轴承故障识别

Fault Diagnosis of Rolling Bearings Based on Weibull Distribution and Fuzzy C Means Clustering Analysis

提出一种基于威布尔分布与模糊C均值(fuzzy C-means,FCM)聚类算法相结合的滚动轴承故障识别方法。针对不同故障类型的威布尔分布模型的尺度参数、形态参数和威布尔负对数能够较好地刻画轴承运行的状态特性,提取其尺度、形态和威布尔负对数似然函数等3个参数构建表征轴承运行状态的特征向量。模糊C均值根据样本相对于聚类中心的隶属度确定样本的亲疏程度而实现分类。实验中,首先采用组合形态滤波器对滚动轴承原始信号进行降噪,然后建立威布尔分布模型,将提取的特征向量输入模糊C均值分类器进行故障诊断和识别。结果表明,该方法对机械故障诊断识别准确率高,可以作为滚动轴承故障识别的重要手段。

A novel approach to extract fault features and fault diagnosis of rolling bearings based on Weibull distribution model and FCM clustering was proposed.Because the Weibull distribution model parameters such as its scale parameters,shape parameters and its similar function of negative logarithm were more different for different fault types,the running state features of the bearings could be characterized better.The feature vectors representing the rolling bearings running state could be constructed by extracting the three parameters including its scale parameters,shape parameters and its similar function of negative logarithm.FCM clustering was realized by determining the distance degrees of the samples according to the samples membership relative to the clustering center and was more effective.In the test process,the vibration signals of rolling bearings were processed firstly by morphological filtering,and a Weibull distribution model for the denoised signals was set up.Then the extracted feature vectors were transmitted to the classifier of FCM clustering for fault diagnosis and recognition.The results show that the proposed method is more accurate and is an important effective means for fault diagnosis.