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Fault Diagnosis via Fusion of Information from a Case Stream
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. SICS Swedish ICT, Kista, Sweden.ORCID iD: 0000-0002-9890-4918
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
Volvo Construction Equipment, Eskilstuna, Sweden.
SICS Swedish ICT, Kista, Sweden.
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2015 (English)In: Case-Based Reasoning Research and Development. Proceeding of the the 23th International Conference on Case-Based Reasoning (ICCBR-2015), 2015, 275-289 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel approach to fault diagnosis applied to a stream of cases. The approach uses a combination of case-based reasoning and information fusion to do classification. The approach consists of two steps. First, we perform local anomaly detection on-board a machine to identify anomalous individual cases. Then, we monitor the stream of anomalous cases using a stream anomaly detector based on a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR classifier. Thereafter, the individual classifications are aggregated into a composite case with a single prediction using a information fusion method. We compare three information fusion approaches: simple majority vote, weighted majority vote and Dempster-Shafer fusion. As baseline for comparison, we use the classification of the last identified anomalous case in the window as the aggregated prediction.

Place, publisher, year, edition, pages
2015. 275-289 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9343
Series
Lecture Notes in Artificial Intelligence
Keyword [en]
Case-based Reasoning, Information Fusion, Anomaly Detection, Fault Diagnosis
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-29085DOI: 10.1007/978-3-319-24586-7_19ISI: 000367594000019Scopus ID: 2-s2.0-84952023366ISBN: 978-3-319-24585-0 (print)OAI: oai:DiVA.org:mdh-29085DiVA: diva2:855802
Conference
23th International Conference on Case-Based Reasoning (ICCBR-2015), 28th September-30th September 2015, Frankfurt am Main, Germany
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2016-01-28Bibliographically approved
In thesis
1. A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
Open this publication in new window or tab >>A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Heavy-duty machines are equipment constructed for working under rough conditions and their design is meant to withstand heavy workloads. However, the last decades technical development in cheap electronically components have lead to an increase of electrical systems in traditionally mainly mechanical systems of heavy-duty machines. As the complexity of these machines increases, so does the complexity of detecting and diagnosing machine faults. However, the addition of new electrical systems, such as on-board computational power and telematics, makes it possible to add new sensors that measure signals relevant for fault detection and diagnosis, and to process signals on-board or off-board the machines.

In this thesis, we address the diagnostic problem by investigating data-driven methods for remote diagnosis of heavy-duty machines, where a part of the analysis is performed on-board the machine (fault detection), while another part is performed off-board the machine (fault classification). We propose a diagnostic framework where we use a novel combination of methods for each step in the diagnosis. On-board the machine, we have used logistic regression as an anomaly detector to detect faults that will lead to a stream of individual cases classified as anomalous or not. Then, either on-board or off-board, we can use a probabilistic anomaly detector to identify whether the stream of cases is truly anomalous when we look at the stream of cases as a group. The anomalous group of cases is called a composite case. Thereafter, off-board the machine, each anomalous individual case is classified into a fault type using a case-based reasoning approach to fault diagnosis. In the final step, we fuse the individual classifications into a single aggregated classification for the composite case. In order to be able to assess the reliability of a diagnosis, we also propose a novel case-based approach to estimating the reliability of probabilistic predictions. It can, for instance, be used for assessing the confidence of the classification of a composite case given historical data of the predictive reliability.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2015
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 189
Series
SICS Dissertation Series, ISSN 1101-1335 ; 73
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29097 (URN)978-91-7485-234-9 (ISBN)
Public defence
2015-11-10, Omega, Mälardalens högskola, Västerås, 14:00 (English)
Opponent
Supervisors
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2015-10-15Bibliographically approved

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Olsson, TomasXiong, NingFunk, Peter

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