Article | The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; Örebro; Sweden | Ideas for Fault Detection Using Relation Discovery

Title:
Ideas for Fault Detection Using Relation Discovery
Author:
Slawomir Nowaczyk: Halmstad University Rune Prytz: Volvo Group Trucks Technology Stefan Byttner: Halmstad University
Download:
Full text (pdf)
Year:
2012
Conference:
The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; Örebro; Sweden
Issue:
071
Article no.:
001
Pages:
1-6
No. of pages:
6
Publication type:
Abstract and Fulltext
Published:
2012-05-14
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


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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.

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

Author:
Slawomir Nowaczyk, Rune Prytz, Stefan Byttner
Title:
Ideas for Fault Detection Using Relation Discovery
References:

[1] S. Byttner; T. R¨ognvaldsson; and M. Svensson. Consensus self-organized models for fault detection (COSMO). Engineering Applications of Artificial In- telligence; 24(5):833–839; 2011.


[2] S.H. D’Silva. Diagnostics based on the statistical correlation of sensors. Technical Report 2008-01-0129; Society of Automotive Engineers (SAE); 2008.


[3] Monson H. Hayes. Statistical Digital Signal Processing and Modeling. John Wiley & Sons; Inc.; 1996.


[4] H. Kargupta et al. VEDAS: A mobile and distributed data stream mining system for real-time vehicle monitoring. In Int. SIAM Data Mining Conference; 2003.


[5] J. Lacaille and E. Come. Visual mining and statistics for turbofan engine fleet. In IEEE Aerospace Conf.; 2011.


[6] Rune Prytz; S lawomir Nowaczyk; and Stefan Byttner. Towards relation discovery for diagnostics. In Proceed- ings of the First International Workshop on Data Min- ing for Service and Maintenance; pages 23–27; 2011.


[7] R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society; 58; 1996.


[8] G. Vachkov. Intelligent data analysis for performance evaluation and fault diagnosis in complex systems. In IEEE International Conference on Fuzzy Systems; pages 6322–6329; July 2006.


[9] Y. Zhang; G.W. Gantt; et al. Connected vehicle diagnostics and prognostics; concept; and initial practice. IEEE Transactions on Reliability; 58(2); 2009.

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

Author:
Slawomir Nowaczyk, Rune Prytz, Stefan Byttner
Title:
Ideas for Fault Detection Using Relation Discovery
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