| Abstract
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<p>Modern vehicles have increasing a … <p>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.</p><p>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.</p>ter than the echo state network.</p>
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| Author
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Yuantao Fan +
, Sławomir Nowaczyk +
, Thorsteinn Rögnvaldsson +
, Eric Aislan Antonelo +
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| Conference
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Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016
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| Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:948970
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| EndPage
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578 +
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| HostPublication
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PHME 2016 : Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016 +
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| Projects
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In4Uptime +
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| PublicationType
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Conference Paper +
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| Publisher
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PHM Society +
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| StartPage
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568 +
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| Title
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Predicting Air Compressor Failures with Echo State Networks +
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| Year
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2016 +
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| Has queryThis property is a special property in this wiki.
|
Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
, Publications:Predicting Air Compressor Failures with Echo State Networks +
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| Categories |
Publication +
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| Modification dateThis property is a special property in this wiki.
|
30 September 2016 20:41:48 +
|
