Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

Resumen

The proposed model of this paper is for the bearing fault diagnosis of industrial rotating machinery. Specifically, the general fault diagnosis model only can predict the bearing fault based on the predefined number of stored fault information. The proposed approach provides an online fault diagnosis process, where unknown faults are detected and updated with knowledge of the proposed diagnosis system.