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A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-9890-4918
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: urn:nbn:se:mdh:diva-29097ISBN: 978-91-7485-234-9 (print)OAI: oai:DiVA.org:mdh-29097DiVA: diva2:855923
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
List of papers
1. Fault Diagnosis via Fusion of Information from a Case Stream
Open this publication in new window or tab >>Fault Diagnosis via Fusion of Information from a Case Stream
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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.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9343
Series
Lecture Notes in Artificial Intelligence
Keyword
Case-based Reasoning, Information Fusion, Anomaly Detection, Fault Diagnosis
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29085 (URN)10.1007/978-3-319-24586-7_19 (DOI)000367594000019 ()2-s2.0-84952023366 (Scopus ID)978-3-319-24585-0 (ISBN)
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
2. Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches
Open this publication in new window or tab >>Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches
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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. 

Keyword
Case-based Reasoning, Machine Learning, Signal Processing, Fault Diagnosis
National Category
Computer Science
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)
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2015-10-06Bibliographically approved
3. A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
Open this publication in new window or tab >>A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
2015 (English)In: Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, Hollywood, Florida, United States: AAAI , 2015, 434-439 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

Place, publisher, year, edition, pages
Hollywood, Florida, United States: AAAI, 2015
Keyword
Anomaly Detection, Anomaly Aggregation, Collective Anomaly, Group Anomaly
National Category
Engineering and Technology Computer and Information Science
Identifiers
urn:nbn:se:mdh:diva-28160 (URN)
Conference
The 28th International FLAIRS Conference Flairs-28, 18 May 2015, Hollywood, Florida, United States
Projects
ITS-EASY Post Graduate School for Embedded Software and Systems
Available from: 2015-06-08 Created: 2015-06-08 Last updated: 2015-09-22Bibliographically approved
4. Case-Based Reasoning for Explaining Probabilistic Machine Learning
Open this publication in new window or tab >>Case-Based Reasoning for Explaining Probabilistic Machine Learning
2014 (English)In: International Journal of Computer Science & Information Technology (IJCSIT), ISSN 0975-4660, E-ISSN 0975-3826, Vol. 6, no 2, 87-101 p.Article in journal (Refereed) Published
Abstract [en]

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As 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. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

Keyword
Case-based Reasoning, Case-based Explanation, Artificial Intelligence, Decision Support, Machine Learning
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-26442 (URN)10.5121/ijcsit (DOI)
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsCREATE ITEA2
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2017-12-05Bibliographically approved
5. Explaining probabilistic fault diagnosis and classification using case-based reasoning
Open this publication in new window or tab >>Explaining probabilistic fault diagnosis and classification using case-based reasoning
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.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8765
Keyword
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:nbn:se:mdh:diva-27478 (URN)2-s2.0-84921634116 (Scopus ID)978-3-319-11208-4 (ISBN)978-3-319-11209-1 (ISBN)
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

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