Difference between revisions of "Publications:Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data"
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{{PublicationSetupTemplate|Author=Rune Prytz, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Stefan Byttner | {{PublicationSetupTemplate|Author=Rune Prytz, Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Stefan Byttner | ||
|PID=788708 | |PID=788708 | ||
| − | |Name=Prytz, Rune | + | |Name=Prytz, Rune (runpry) (0000-0001-8255-1276) (Volvo Group Trucks Technology, Gothenburg, Sweden);Nowaczyk, Sławomir (slanow) (0000-0002-7796-5201) (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));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));Byttner, Stefan (stefan) (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=Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data | |Title=Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data | ||
|PublicationType=Journal Paper | |PublicationType=Journal Paper | ||
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|Journal=Engineering applications of artificial intelligence | |Journal=Engineering applications of artificial intelligence | ||
|JournalISSN=0952-1976 | |JournalISSN=0952-1976 | ||
| − | |Status= | + | |Status=published |
|Volume=41 | |Volume=41 | ||
|Issue= | |Issue= | ||
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|ISI= | |ISI= | ||
|PMID= | |PMID= | ||
| − | |ScopusId= | + | |ScopusId=2-s2.0-84926374379 |
|NBN=urn:nbn:se:hh:diva-27808 | |NBN=urn:nbn:se:hh:diva-27808 | ||
|LocalId= | |LocalId= | ||
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|Projects=in4uptime | |Projects=in4uptime | ||
|Notes=<p>The authors thank Vinnova (Swedish Governmental Agency for Innovation Systems), AB Volvo, Halmstad University, and the Swedish Knowledge Foundation for financial support for doing this research.</p> | |Notes=<p>The authors thank Vinnova (Swedish Governmental Agency for Innovation Systems), AB Volvo, Halmstad University, and the Swedish Knowledge Foundation for financial support for doing this research.</p> | ||
| − | |Abstract=<p>Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.</p><p>Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain.</p> | + | |Abstract=<p>Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.</p><p>Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd.</p> |
|Opponents= | |Opponents= | ||
|Supervisors= | |Supervisors= | ||
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|CreatedDate=2015-02-16 | |CreatedDate=2015-02-16 | ||
|PublicationDate=2015-02-16 | |PublicationDate=2015-02-16 | ||
| − | |LastUpdated=2015- | + | |LastUpdated=2015-04-20 |
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:788708}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:788708}} | ||
<div style='display: none'> | <div style='display: none'> | ||
Latest revision as of 22:39, 30 September 2016
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| Title | Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data |
|---|---|
| Author | |
| Year | 2015 |
| PublicationType | Journal Paper |
| Journal | Engineering applications of artificial intelligence |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1016/j.engappai.2015.02.009 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:788708 |
| Abstract | Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit. Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd. |
