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Evaluation of Micro-flaws in Metallic Material Based on A Self-Organized Data-driven Approach
Shanghai University of Engineering Science, Shanghai, China & Nanjing University, Nanjing, China.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3034-6630
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.ORCID iD: 0000-0002-7796-5201
2016 (English)In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), IEEE conference proceedings, 2016Conference paper, (Refereed)
Abstract [en]

Evaluating the health condition of a material that could potentially contain micro-flaws is a common and important application within the field of non-destructive testing. Examples of such micro-defects include dislocation, fatigue cracks or impurities and are often hard to detect. The ability to precisely measure their type, size and position is a prerequisite for estimating the remaining useful life of the component. One technique that was shown successful in the past is based on traditional ultrasonic testing methods. In most cases, inner micro-flaws induce slight changes of acoustic wave spectrum components. However, these changes are often difficult to detect directly, as they tend to exhibit features that are most naturally analyzed using statistical and probabilistic methods. In this paper we apply Consensus Self-Organizing Models (COSMO) method to detect micro-flaws in metallic material. This approach is essentially an unsupervised deviation detection method based on the concept of "wisdom of the crowd". This method is used to analyze the spectrum of acoustic waves received by the transducer attached on the surface of material being analyzed. We have modeled a steel board with micro-cracks and collected time-series of acoustic echo response, at different positions on material's surface. The experimental results show that the COSMO method is able to detect and locate micro-flaws. © 2016 IEEE

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016.
Keyword [en]
Non-destructive testing, ultrasonic, micro-defects
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-31646DOI: 10.1109/ICPHM.2016.7542868ISBN: 978-1-5090-0382-2 (print)OAI: oai:DiVA.org:hh-31646DiVA: diva2:948974
Conference
2016 IEEE International Conference on Prognostics and Health Management, Carleton University, Ottawa, ON, Canada, June 20-22, 2016
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2016-11-28Bibliographically approved
In thesis
1. A Self-Organized Fault Detection Method for Vehicle Fleets
Open this publication in new window or tab >>A Self-Organized Fault Detection Method for Vehicle Fleets
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A fleet of commercial heavy-duty vehicles is a very interesting application arena for fault detection and predictive maintenance. With a highly digitized electronic system and hundreds of sensors mounted on-board a modern bus, a huge amount of data is generated from daily operations.

This thesis and appended papers present a study of an autonomous framework for fault detection, using the data gathered from the regular operation of vehicles. We employed an unsupervised deviation detection method, called Consensus Self-Organising Models (COSMO), which is based on the concept of ‘wisdom of the crowd’. It assumes that the majority of the group is ‘healthy’; by comparing individual units within the group, deviations from the majority can be considered as potentially ‘faulty’. Information regarding detected anomalies can be utilized to prevent unplanned stops.

This thesis demonstrates how knowledge useful for detecting faults and predicting failures can be autonomously generated based on the COSMO method, using different generic data representations. The case study in this work focuses on vehicle air system problems of a commercial fleet of city buses. We propose an approach to evaluate the COSMO method and show that it is capable of detecting various faults and indicates upcoming air compressor failures. A comparison of the proposed method with an expert knowledge based system shows that both methods perform equally well. The thesis also analyses the usage and potential benefits of using the Echo State Network as a generic data representation for the COSMO method and demonstrates the capability of Echo State Network to capture interesting characteristics in detecting different types of faults.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2016. 116 p.
Series
Halmstad University Dissertations, 27
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-32489 (URN)978-91-87045-57-8 (ISBN)978-91-87045-56-1 (ISBN)
Presentation
2016-12-16, Halda, Kristian IV:s väg 3, 301 18 Halmstad, Halmstad, 10:00 (English)
Opponent
Supervisors
Projects
In4Uptime
Funder
VINNOVA
Available from: 2016-11-28 Created: 2016-11-25 Last updated: 2016-11-28Bibliographically approved

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