Su-Qun Caoand Limin Luo, Xinggang Ma, Youfu Zhang and Fupeng Yi

Machine learning approaches are generally adopted in many fields including data mining, image processing, intelligent fault diagnosis etc. As a classic unsupervised learning technology, fuzzy C-means cluster analysis plays a vital role in machine learning based intelligent fault diagnosis. With the rapid development of science and technology, the monitoring signal data is numerous and keeps growing fast. Only typical fault samples can be obtained and labeled. Thus, how to apply semi-supervised learning technology in fault diagnosis is significant for guaranteeing the equipment safety. According to this, a novel fault diagnosis method based on semi-supervised fuzzy C-means(SFCM) cluster analysis is proposed. Experimental results on Iris data set and the steel plates faults data set show that this method is superior to traditional fuzzy C-means clustering analysis.

Fault Diagnosis, Unsupervised pattern, Cluster Analysis, Semi-supervised Learning