Semi-supervised Kernel-based Fuzzy Clustering for Gear Outlier Detection

Abstract

Kernel clustering is investigated in mechanical fault detection,and a semi-supervised kernel-based fuzzy clustering method is presented for gear fault early detection. The difficulty in early detection of mechanical incipient fault is to extract the weak fault information in noises. The semi-supervised kernel clustering method utilizes a few of known samples,combined with a larger amount of unknown samples to perform semi-supervised learning,and obtains good efficiency. The experiments are conducted on a gearbox,where a surface defect of tooth pitting is introduced.The result of semi-supervised kernel clustering is compared with that of unsupervised kernel clustering,which demonstrates the superiority of the semi-supervised method for gear failure detection.