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Explaining probabilistic fault diagnosis and classification using case-based reasoning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-9890-4918
SICS Swedish ICT, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2014 (English)In: Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings, 2014, 360-374 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

Place, publisher, year, edition, pages
2014. 360-374 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8765
Keyword [en]
Case-based Explanation, Classification, Machine Learning, Artificial intelligence, Classification (of information), Computer aided diagnosis, Fault detection, Learning systems, Probability, Probability distributions, Case based, Case-based approach, Logistic regressions, Probabilistic classification, Probabilistic classifiers, Probabilistic fault diagnosis, Probability modeling, Similarity metrics, Case based reasoning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-27478Scopus ID: 2-s2.0-84921634116ISBN: 978-3-319-11208-4 (print)ISBN: 978-3-319-11209-1 (print)OAI: oai:DiVA.org:mdh-27478DiVA: diva2:786657
Conference
22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014.
Available from: 2015-02-06 Created: 2015-02-06 Last updated: 2015-09-22Bibliographically 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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Citation style
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