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An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline

Modern cars are equipped with plenty of electronic devices called Electronic Control Units (ECU). ECUs collect diagnostic data from a car’s components such as the engine, brakes etc. These data are then processed, and the appropriate information is communicated to the driver. From the point of view...

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Detalles Bibliográficos
Autores principales: Kowalik, Bartosz, Szpyrka, Marcin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514915/
https://www.ncbi.nlm.nih.gov/pubmed/33267140
http://dx.doi.org/10.3390/e21040426
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author Kowalik, Bartosz
Szpyrka, Marcin
author_facet Kowalik, Bartosz
Szpyrka, Marcin
author_sort Kowalik, Bartosz
collection PubMed
description Modern cars are equipped with plenty of electronic devices called Electronic Control Units (ECU). ECUs collect diagnostic data from a car’s components such as the engine, brakes etc. These data are then processed, and the appropriate information is communicated to the driver. From the point of view of safety of the driver and the passengers, the information about the car faults is vital. Regardless of the development of on-board computers, only a small amount of information is passed on to the driver. With the data mining approach, it is possible to obtain much more information from the data than it is provided by standard car equipment. This paper describes the environment built by the authors for data collection from ECUs. The collected data have been processed using parameterized entropies and data mining algorithms. Finally, we built a classifier able to detect a malfunctioning thermostat even if the car equipment does not indicate it.
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spelling pubmed-75149152020-11-09 An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline Kowalik, Bartosz Szpyrka, Marcin Entropy (Basel) Article Modern cars are equipped with plenty of electronic devices called Electronic Control Units (ECU). ECUs collect diagnostic data from a car’s components such as the engine, brakes etc. These data are then processed, and the appropriate information is communicated to the driver. From the point of view of safety of the driver and the passengers, the information about the car faults is vital. Regardless of the development of on-board computers, only a small amount of information is passed on to the driver. With the data mining approach, it is possible to obtain much more information from the data than it is provided by standard car equipment. This paper describes the environment built by the authors for data collection from ECUs. The collected data have been processed using parameterized entropies and data mining algorithms. Finally, we built a classifier able to detect a malfunctioning thermostat even if the car equipment does not indicate it. MDPI 2019-04-22 /pmc/articles/PMC7514915/ /pubmed/33267140 http://dx.doi.org/10.3390/e21040426 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kowalik, Bartosz
Szpyrka, Marcin
An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
title An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
title_full An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
title_fullStr An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
title_full_unstemmed An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
title_short An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
title_sort entropy-based car failure detection method based on data acquisition pipeline
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514915/
https://www.ncbi.nlm.nih.gov/pubmed/33267140
http://dx.doi.org/10.3390/e21040426
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