]> 2026-04-05T00:38:12+02:00 Publications:Towards relation discovery for diagnostics 500 en <p>It is difficult to implement predictive maintenance in the automotive industry as it looks today, since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of vehicle configurations and usage patterns. However, it is now becoming feasible to obtain and analyse on-board data on vehicles as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific vehicle configurations and usage. In order to be useful, though, such methods need to be able to detect interesting relations between a large number of available signals. This paper presents an unsupervised method for discovering useful relations between measured signals in a Volvo truck, both during normal operations and when a fault has occurred. The interesting relationships are found in a two-step procedure. In the first step, we identify a set of “good” models, by establishing an MSE threshold over the complete data set. In the second step, we estimate model parameters over time, in order to capture the dynamic behaviour of the system. We use two different approaches here, the LASSO method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.</p> 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining http://dx.doi.org/10.1145/2018673.2018678 http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:437123 27 Proceedings of the First International Workshop on Data Mining for Service and Maintenance Conference Paper 23 Towards relation discovery for diagnostics 2011 2016-09-30T20:39:57Z 2457662.3610764 Towards relation discovery for diagnostics 500 0 plainlist list 1 [[:Publications:Towards relation discovery for diagnostics]] Towards relation discovery for diagnostics# QUERYa7533f6c55321a6baa99a38cd4a1277a 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Towards relation discovery for diagnostics# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Towards relation discovery for diagnostics# ERRe24f17e090c98448f3de99d03182b7b6