NDLI: Fault detection based on robust independent component analysis and support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Ying Feng Jin Zhao Yu Ji Jie Xu Zhongyu Shen
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Autom. Eng., Nanjing Normal Univ., Nanjing, China (Ying Feng; Jin Zhao; Yu Ji; Jie Xu; Zhongyu Shen)
Abstract	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.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	1117
Ending Page	1122
File Size	284929
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561092
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fault detection RobustICA Noise Estimation Independent component analysis Wavelet analysis Robustness Kurtosis Iterative optimization Kernel
Content Type	Text
Resource Type	Article

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