A comparative study of artificial neural networks and support vector machine for fault diagnosis

Projekt:

JVTC

Sammanfattning:
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

Författare: Yuan Fuqing ; Uday Kumar ; Diego Galar
Utgivare: IJPE
Utgivningsdatum: 2013
Diarienummer: TRV 2011/58769
ISSN: 0973-1318
Antal sidor: 12
Språk: Engelska
Kontaktperson: Per Olof Larsson Kråik, UHjbs