A Comparative Study of Artificial Neural Networks and Support Vector Machine for Fault Diagnosis
Volume 9, Number 1, January 2013 - Paper 05 - pp. 49 - 60
YUAN FUQING, UDAY KUMAR AND DIEGO GALARDivision of Operation and Maintenance, Luleå University of Technology, SE-971 87 Lulea, Sweden
(Received on November 20, 2011, revised on September 23, 2012)
Fault detection is a crucial step in condition based maintenance requiring. The importance of fault diagnosis necessitates an efficient and effective failure pattern identification method. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) emerging as prospective pattern recognition techniques in fault diagnosis have been showing its adaptability, flexibility and efficiency. Regardless of variants of the two techniques, this paper discusses the principle of the two techniques, and discusses their theoretical similarity and difference. Eventually using the commonest ANN, SVM, a case study is presented for fault diagnosis using a wide used bearing data. Their performances are compared in terms of accuracy, computational cost and stability.
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