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|Authors:||Slawomir Nowaczyk: Halmstad University|
|Rune Prytz: Volvo Group Trucks Technology|
|Stefan Byttner: Halmstad University|
|Publication title:||Ideas for Fault Detection Using Relation Discovery|
|Conference:||The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), 14–15 May 2012, Örebro, Sweden|
|Publication type:||Abstract and Fulltext|
|Abstract:||Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms.|
Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions.
In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.
|No. of pages:||6|
|Series:||Linköping Electronic Conference Proceedings|
|Publisher:||Linköping University Electronic Press, Linköpings universitet|
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