NDLI: Complex dynamical system fault diagnosis based on multiple ANFIS using independent component

Content Provider	IEEE Xplore Digital Library
Author	Akhlaghi, P. Kashanipour, A.R. Salahshoor, K.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Islamic Azad Univ., Tehran (Akhlaghi, P.; Kashanipour, A.R.)
Abstract	In this paper, an online fault diagnosis for a complex dynamical systems integrating adaptive neuro-fuzzy inference system (ANFIS) and using independent component analysis (ICA) for feature extracting is presented. In this approach, using ICA provide salient features selected from raw measured data sets. Subsequently, the most superior extracted features are fed into multiple ANFIS in order to identify different abnormal cases. Using multiple ANFIS units each dedicated to one fault, significantly reduces the scale and complexity of the system and speeds up the training of the network. This data-driven method is quite simple fast and expandable. Chemical dynamic processes are so complex that they are presently ahead of theoretical methods from fundamental physical standpoint. Distillation column is used as a complex benchmark problem to evaluate the proposed algorithm. Experimental results from this simulated nonlinear MIMO dynamical system demonstrated the effectiveness of the method. This procedure is applicable to a variety of industrial applications in which continuous on-line monitoring and diagnosis are necessary.
Starting Page	1798
Ending Page	1803
File Size	1717674
Page Count	6
File Format	PDF
ISBN	9781424425044
DOI	10.1109/MED.2008.4602207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automation Conferences
Content Type	Text
Resource Type	Article

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