Fault detection based on robust independent component analysis and support vector machines
Ying Feng, Jin Zhou Zhao, Yu Ji, Jie Xu, Zhongyu Shen · 2013
This study aims to develop an intelligent algorithm by integrating robust independent component analysis (RobustICA) and support vector machines (SVMs). According to different characteristics of source signals including real or complex, super-Gaussian or sub-Gaussian and pollution of signal noise, a new method for fault detection that uses RobustICA based on kurtosis is put forward in this paper. The basic idea of the approach is to use RobustICA optimized by iterative technique to separate independent components which drive a process after wavelet de-noising of the original data. On this basis, statistics are established for fault detection and the kernel density estimation is used in calculating the confidence limit of statistics. After that, support vector machines (SVMs) is utilized to classify the faults. The simulation results of signal experiment and TE model clearly show the effectiveness and advantages of the proposed method in comparison to FastICA method.