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

Department of Applied Computer Science, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 426;
Received: 11 March 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
PDF [630 KB, uploaded 22 April 2019]


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. View Full-Text
Keywords: car failure detection; entropy; data mining car failure detection; entropy; data mining

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Kowalik, B.; Szpyrka, M. An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline. Entropy 2019, 21, 426.

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