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.
The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; √Ėrebro; Sweden
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