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Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches
RISE, Swedish ICT, SICS. Decisions, Networks and Analytics lab.
RISE, Swedish ICT, SICS. Decisions, Networks and Analytics lab.
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Number of Authors: 9
2014 (English)Conference paper, (Refereed)
Abstract [en]

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 making. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely integrated 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.

Place, publisher, year, edition, pages
2014, 11.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-24350OAI: oai:DiVA.org:ri-24350DiVA: diva2:1043431
Conference
Workshop on Synergies between CBR and Data Mining at 22nd International Conference on Case-Based Reasoning
Available from: 2016-10-31 Created: 2016-10-31Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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