New clustering algorithm-based fault diagnosis using compensation distance evaluation technique

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.