基于不完全信息的轴承故障聚类识别方法

联合收获机中零部件繁多及滚珠滑失等因素,导致监测信号中轴承组件的特征频率并非总能找到,进而影响了故障诊断的正确率。为了解决该问题,提出了一种基于不完全信息的轴承故障聚类识别方法。该方法将特征频率显著的样本作为先验信息,利用这些信息进行相关成分分析,从而给相关程度高的特征赋予大的权重,然后利用改进的半监督聚类算法对所有样本进行聚类识别。其中,提出了近邻扩展方法对先验信息进行扩充,增加了目标函数惩罚环节对聚类过程予以指导。将所提方法应用于联合收获机的轴承滚珠和外圈故障识别,与其它几种聚类方法相比,故障识别率提高了2.78%~7.22%。

A Clustering Approach Based on Partial Information for Recognizing Bearing Fault

Due to the reasons of too many components in combine harvester and the skid of rolling balls, the characteris-tic frequencies of bearing assembly in monitoring signals are not always clearly existing, which causes the low accuracy of fault diagnosis.Hence, a clustering approach based on partial information is proposed to tackle this problem.This ap-proach sets these samples with clearly characteristic frequencies as priori information, and then uses them to make rele-vant component analysis to high weights to relevant dimensions.This approach also design an advanced clustering algo-rithm to recognize all the samples, wherein an extension strategy based on neighborhood is presented to obtain more priori information, and a penalty step is added to the objective function to guiding the clustering.The fault data on ball and outer race of bearing of a combine harvester is used to validate the proposed approach.The results show that our proposed approach works better than others, where the recognition accuracy is higher than others from 2.78%to 7.22%.