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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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