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Fault Diagnosis of Heavy Duty Machines : Automatic Transmission Clutches
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- Olsson, Tomas (author)
- RISE,Mälardalens högskola,Inbyggda system,SICS Swedish ICT, Sweden,SICS
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- Källström, Elisabeth (author)
- Volvo Construction Equipment, Eskilstuna, Sweden
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- Gillblad, Daniel (author)
- RISE,Decisions, Networks and Analytics lab,SICS Swedish ICT, Sweden
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- (creator_code:org_t)
- 2014
- 2014
- English.
- In: Proceedings of the ICCBR 2014 Workshops.
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision mak- ing. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely in- tegrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Case-based Reasoning
- Machine Learning
- Signal Processing
- Fault Diagnosis
- Computer Science
- datavetenskap
Publication and Content Type
- ref (subject category)
- kon (subject category)
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- Olsson, Tomas
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- Gillblad, Daniel
- Funk, Peter
- Lindström, John
- Håkansson, Lars
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- Lundin, Joakim
- Svensson, Magnus
- Larsson, Jonas
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- Mälardalen University
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