Abstract: Machinery is always running under multiple operating regimes,and it is difficult to collect the specific fault samples to train the learning machine,which also leads to the low accuracy in fault detection and limits the generalization of the intelligent fault detection methods.Combining the support vector data description and the kernel-based fuzzy C-means clustering,a semi-supervised SVDD-KFCM algorithm for machine defect detection was proposed.Experiment results demonstrate that the proposed scheme is capable of detecting the incipient bearing fault effectively and correctly.

Key words: SVDD,KFCM,Fault detection,Semi-supervised learning