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Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation
Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0003-0808-052X
School of Engineering, Ulster University, Newtownabbey, UK; Electrical and Computer Systems Engineering, School of Engineering, Monash University Malaysia, Malaysia.
Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för systemteknik, Datorteknik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0003-4965-1077
2018 (engelsk)Inngår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 80, s. 146-156Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. This paper proposes a hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. The proposed method is verified using a physical model and data collected from an internal combustion engine.

sted, utgiver, år, opplag, sider
Elsevier, 2018. Vol. 80, s. 146-156
Emneord [en]
Fault diagnosis, Fault isolation, Machine learning, Artificial intelligence, Classification
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-151296DOI: 10.1016/j.conengprac.2018.08.013ISI: 000447483500014OAI: oai:DiVA.org:liu-151296DiVA, id: diva2:1248561
Merknad

Funding agencies: Volvo Car Corporation in Gothenburg, Sweden

Tilgjengelig fra: 2018-09-17 Laget: 2018-09-17 Sist oppdatert: 2019-09-23

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Fulltekst tilgjengelig fra 2020-09-09 11:37
Tilgjengelig fra 2020-09-09 11:37

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Frisk, ErikKrysander, Mattias

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