Publikation: Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering
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2016
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BOSTRÖM, Henrik, ed., Arno KNOBBE, ed., Carlos SOARES, ed., Panagiotis PAPAPETROU, ed.. Advances in Intelligent Data Analysis XV. Cham: Springer International Publishing, 2016, pp. 398-402. Lecture Notes in Computer Science. 9897. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-46348-3. Available under: doi: 10.1007/978-3-319-46349-0_36
Zusammenfassung
This paper demonstrates an approach in data analysis to minimize overall maintenance costs for the air pressure system of Scania trucks. Feature creation on histograms was used. Randomly chosen subsets of attributes were then evaluated to generate an order and a final subset of features. Finally, a Random Forest was applied and fine-tuned. The results clearly show that data analysis in the field is beneficial and improves upon the naive approaches of checking every truck or no truck until failure.
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Fachgebiet (DDC)
004 Informatik
Schlagwörter
Data mining, Feature extraction, Dimension reduction, Random forest
Konferenz
Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, 13. Okt. 2016 - 15. Okt. 2016, Stockholm, Sweden
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GONDEK, Christopher, Daniel HAFNER, Oliver R. SAMPSON, 2016. Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering. Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016. Stockholm, Sweden, 13. Okt. 2016 - 15. Okt. 2016. In: BOSTRÖM, Henrik, ed., Arno KNOBBE, ed., Carlos SOARES, ed., Panagiotis PAPAPETROU, ed.. Advances in Intelligent Data Analysis XV. Cham: Springer International Publishing, 2016, pp. 398-402. Lecture Notes in Computer Science. 9897. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-46348-3. Available under: doi: 10.1007/978-3-319-46349-0_36BibTex
@inproceedings{Gondek2016Predi-37212,
year={2016},
doi={10.1007/978-3-319-46349-0_36},
title={Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering},
number={9897},
isbn={978-3-319-46348-3},
issn={0302-9743},
publisher={Springer International Publishing},
address={Cham},
series={Lecture Notes in Computer Science},
booktitle={Advances in Intelligent Data Analysis XV},
pages={398--402},
editor={Boström, Henrik and Knobbe, Arno and Soares, Carlos and Papapetrou, Panagiotis},
author={Gondek, Christopher and Hafner, Daniel and Sampson, Oliver R.}
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<dcterms:abstract xml:lang="eng">This paper demonstrates an approach in data analysis to minimize overall maintenance costs for the air pressure system of Scania trucks. Feature creation on histograms was used. Randomly chosen subsets of attributes were then evaluated to generate an order and a final subset of features. Finally, a Random Forest was applied and fine-tuned. The results clearly show that data analysis in the field is beneficial and improves upon the naive approaches of checking every truck or no truck until failure.</dcterms:abstract>
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