Difference between revisions of "Publications:Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet"
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| − | |Name=Fan, Yuantao | + | |Name=Fan, Yuantao (yuafan) (0000-0002-3034-6630) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Nowaczyk, Sławomir (slanow) (0000-0002-7796-5201) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Laboratoriet för intelligenta system (6703));Rögnvaldsson, Thorsteinn (denni) (0000-0001-5163-2997) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650)) |
|Title=Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet | |Title=Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet | ||
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|CreatedDate=2015-11-24 | |CreatedDate=2015-11-24 | ||
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Latest revision as of 22:41, 30 September 2016
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| Title | Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet |
|---|---|
| Author | |
| Year | 2015 |
| PublicationType | Journal Paper |
| Journal | Frontiers in Artificial Intelligence and Applications |
| HostPublication | |
| Conference | The 13th Scandinavian Conference on Artificial Intelligence (SCAI), Halmstad University, Halmstad, Sweden, 5-6 November, 2015 |
| DOI | http://dx.doi.org/10.3233/978-1-61499-589-0-58 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:873690 |
| Abstract | In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure. In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method. |
