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A comparative study of artificial neural networks and support vector machine for fault diagnosis
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-4107-0991
2013 (engelsk)Inngår i: International Journal of Pedagogy, Innovation and New Technologies, ISSN 0973-1318, E-ISSN 2392-0092, Vol. 9, nr 1, s. 49-60Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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

sted, utgiver, år, opplag, sider
2013. Vol. 9, nr 1, s. 49-60
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-4763Lokal ID: 2c0f1f89-3ade-4c4a-a92f-ceffaf48d367OAI: oai:DiVA.org:ltu-4763DiVA, id: diva2:977637
Merknad
Validerad; 2013; 20121218 (andbra)Tilgjengelig fra: 2016-09-29 Laget: 2016-09-29 Sist oppdatert: 2017-11-24bibliografisk kontrollert

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