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Using Histograms to Find Compressor Deviations in Bus Fleet Data
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), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5163-2997
2014 (English)In: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, 123-132 p.Conference paper, (Refereed)
Abstract [en]

Cost effective methods for predictive maintenance are increasingly demanded in the automotive industry. One solution is to utilize the on-board signals streams on each vehicle and build self-organizing systems that discover data deviations within a fleet. In this paper we evaluate histograms as features for describing and comparing individual vehicles. The results are based on a long-term field test with nineteen city buses operating around Kungsbacka in Halland. The purpose of this work is to investigate ways of discovering abnormal behaviors and irregularities between histograms of on-board signals, here specifically focusing on air pressure. We compare a number of distance measures and analyze the variability of histograms collected over different time spans. Clustering algorithms are used to discover structure in the data and track how this changes over time. As data are compared across the fleet, observed deviations should be matched against (often imperfect) reference data coming from workshop maintenance and repair databases.

Place, publisher, year, edition, pages
Swedish Artificial Intelligence Society (SAIS) , 2014. 123-132 p.
Keyword [en]
Predictive maintenance, Diagnostics, Deviation detection
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-26572OAI: oai:DiVA.org:hh-26572DiVA: diva2:749489
Conference
The Swedish AI Society (SAIS) Workshop 2014, Stockholm, Sweden, May 22-23, 2014
Available from: 2014-09-24 Created: 2014-09-24 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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Citation style
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