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Predicting Air Compressor Failures with Echo State Networks
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
Federal University of Santa Catarina, Florianópolis, Brazil.
2016 (English)In: PHME 2016: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016 / [ed] Ioana Eballard, Anibal Bregon, PHM Society , 2016, p. 568-578Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to detect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment.

A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One approach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal's characteristics and work well on simulated data. However, the histogram model, despite being simpler, handles the deviations in real data better than the echo state network.

Place, publisher, year, edition, pages
PHM Society , 2016. p. 568-578
Keywords [en]
predictive maintenance, fault detection, Vehicle diagnostics, reservoir model, echo state network
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:hh:diva-31644ISBN: 978-1-936263-21-9 OAI: oai:DiVA.org:hh-31644DiVA, id: diva2:948970
Conference
Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016
Projects
In4Uptime
Funder
VINNOVAAvailable from: 2016-07-14 Created: 2016-07-14 Last updated: 2020-01-10Bibliographically 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. p. 116
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
2. Wisdom of the Crowd for Fault Detection and Prognosis
Open this publication in new window or tab >>Wisdom of the Crowd for Fault Detection and Prognosis
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. 

This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers. 

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2020. p. 87
Series
Halmstad University Dissertations ; 67
National Category
Information Systems
Identifiers
urn:nbn:se:hh:diva-41367 (URN)978-91-88749-43-7 (ISBN)978-91-88749-42-0 (ISBN)
Public defence
2020-01-31, J102 Wigforss, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2020-01-14 Created: 2020-01-10 Last updated: 2020-01-14Bibliographically approved

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Fan, YuantaoNowaczyk, SławomirRögnvaldsson, Thorsteinn

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