New clustering algorithm-based fault diagnosis using compensation distance evaluation technique
New clustering algorithm-based fault diagnosis using compensation distance evaluation technique
复制标题
DOI：
10.1016/j.ymssp.2007.07.013
复制
发表时间：
2008-02-01
影响因子：
8.4
通讯作者：
Chen, Xuefeng
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;He, Zhengjia;Chen, Xuefeng
本文提出了一种基于补偿距离评估技术（CDET）的新型聚类算法的旋转机械故障诊断方法。该算法采用两阶段特征选择和加权技术。根据特征的敏感性，通过CDET计算特征权重，并将其分配给相应的特征，以指示它们在聚类中的不同重要性。特征加权突出了敏感特征的重要性，同时削弱了不敏感特征的干扰。描述了新的聚类算法并将其应用于机车滚子轴承的早期故障和复合故障诊断。诊断结果表明，该算法不仅能够可靠地识别不同类别和严重程度的故障，而且能够可靠地识别复合故障，体现了该算法优越的有效性和实用性。因此，它是旋转机械故障诊断的一种有前途的方法。
This paper presents a fault diagnosis method of rotating machinery based on a new clustering algorithm using a compensation distance evaluation technique (CDET). A two-stage feature selection and weighting technique is adopted in this algorithm. Feature weights are computed via CDET according to the sensitivity of features and assigned to the corresponding features to indicate their different importance in clustering. Feature weighting highlights the importance of sensitive features and simultaneously weakens the interference of insensitive features. The new clustering algorithm is described and applied to incipient fault and compound fault diagnosis of locomotive roller bearings. The diagnosis result shows the algorithm is able to reliably recognise not only different fault categories and severities but also the compound faults, and demonstrates the superior effectiveness and practicability of the algorithm. Therefore, it is a promising approach to fault diagnosis of rotating machinery.