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Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery
Beijing Information Science & Technology University, Beijing, China.
Beijing Information Science & Technology University, Beijing, China.
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Maskinteknisk produktframtagning (MTEK), Funktionella ytor.ORCID-id: 0000-0001-8058-1252
2014 (engelsk)Inngår i: Chinese Journal of Scientific Instrument, ISSN 0254-3087, Vol. 35, nr 12, s. 210-214, artikkel-id 210Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine(PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

sted, utgiver, år, opplag, sider
Beijing: Yiqi Yibiao Xuebao Zazhishe , 2014. Vol. 35, nr 12, s. 210-214, artikkel-id 210
Emneord [en]
fault diagnosis, multi manifold learning, particle swarm optimization, support vector machine
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-30121Scopus ID: 2-s2.0-84937426276OAI: oai:DiVA.org:hh-30121DiVA, id: diva2:890028
Merknad

Funding: Beijing Natural Science Foundation (KZ201211232039); National Natural Science Foundation of China (51275052); Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipalipality (PHR201106132)

Tilgjengelig fra: 2015-12-30 Laget: 2015-12-30 Sist oppdatert: 2016-01-04bibliografisk kontrollert

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