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2014 (English)In: Proceedings of the ICCBR 2014 Workshops, 2014Conference paper, Published 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 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.
Keywords
Case-based Reasoning, Machine Learning, Signal Processing, Fault Diagnosis
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29088 (URN)
Conference
Workshop on Synergies between CBR and Data Mining at the 22nd International Conference on Case-Based Reasoning (CBRDM’14)
2015-09-222015-09-222018-01-11Bibliographically approved