NDLI: Nonlinear Support Vector Machines for Solving the PMC-Based System-Level Fault Diagnosis Problem

Content Provider	IEEE Xplore Digital Library
Author	Elhadef, M.
Copyright Year	2013
Description	Author affiliation: Coll. of Eng. & Comput. Sci., Abu-Dhabi Univ., Abu-Dhabi, United Arab Emirates (Elhadef, M.)
Abstract	This paper deals with the system-level fault diagnosis problem which main objective is to identify faults, in particular permanent ones, in diagnosable systems under the PMC model. The PMC model assumes that each system's node is tested by a subset of the other nodes, and that at most t of these nodes are permanently faulty. Tests performed by faulty nodes are unreliable, and hence, they can incorrectly diagnose fault-free nodes as faulty or faulty ones as fault-free. In this paper, we describe a new nonlinear support vector machines-based (SVMs) diagnosis algorithm, which exploits the off-line learning phase of SVMs to speed up the diagnosis algorithm. The novel diagnosis approach has been implemented and evaluated using randomly generated diagnosable systems. Results from the thorough simulation study demonstrate the effectiveness of the nonlinear SVM-based fault diagnosis algorithm, in terms of diagnosis correctness, latency, and scalability. In addition, extreme faulty situations, where the number of faults is around the bound t, and large diagnosable systems have been also experimented to show the efficiency of the new nonlinear SVM-based diagnosis algorithm.
Starting Page	1
Ending Page	8
File Size	366527
Page Count	8
File Format	PDF
ISBN	9780769550961
DOI	10.1109/CSE.2013.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fault diagnosis Training Adaptation models Fault tolerance PMC model Vectors Kernel Partial syndromes Testing System-level fault diagnosis
Content Type	Text
Resource Type	Article

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