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Publications:Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data
Abstract <p>Predictive maintenance is becomin<p>Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic maintenance schedule, fulfilling specific needs of individual vehicles. Luckily, the same shift of focus, as well as technological advancements in the telecommunication area, make long-term data collection more widespread, delivering the necessary data.</p><p>We have found, however, that the standard attribute-value knowledge representation is not rich enough to capture important dependencies in this domain. Therefore, we are proposing a new rule induction algorithm, inspired by Michalski's classical AQ approach. Our method is aware that data concerning each vehicle consists of time-ordered sequences of readouts. When evaluating candidate rules, it takes into account the composite performance for each truck, instead of considering individual readouts in separation. This allows us more flexibility, in particular in defining desired prediction horizon in a fuzzy, instead of crisp, manner.</p>fuzzy, instead of crisp, manner.</p>
Author Sławomir Nowaczyk + , Rune Prytz + , Thorsteinn Rögnvaldsson + , Stefan Byttner +
Conference 12th Scandinavian Conference on Artificial Intelligence, Aalborg, Denmark, November 20–22, 2013
DOI http://dx.doi.org/10.3233/978-1-61499-330-8-205  +
Diva http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:682966
EndPage 214  +
HostPublication Twelfth Scandinavian Conference on Artificial Intelligence  +
Projects ReDi2Service +
PublicationType Conference Paper  +
Publisher IOS Press  +
Series Frontiers in Artificial Intelligence and Applications ; 257  +
StartPage 205  +
Title Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data  +
Year 2013  +
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Categories Publication  +
Modification dateThis property is a special property in this wiki. 30 September 2016 20:39:16  +
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