An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline
| Autor: | Marcin Szpyrka, Bartosz Kowalik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Real-time computing General Physics and Astronomy lcsh:Astrophysics 02 engineering and technology Data mining algorithm Article law.invention Data acquisition 0203 mechanical engineering law lcsh:QB460-466 Diagnostic data Entropy (information theory) Electronics lcsh:Science Data collection data mining 021001 nanoscience & nanotechnology Thermostat lcsh:QC1-999 020303 mechanical engineering & transports car failure detection lcsh:Q 0210 nano-technology entropy lcsh:Physics |
| Zdroj: | Entropy, Vol 21, Iss 4, p 426 (2019) Entropy Volume 21 Issue 4 |
| ISSN: | 1099-4300 |
| Popis: | Modern cars are equipped with plenty of electronic devices called Electronic Control Units (ECU). ECUs collect diagnostic data from a car&rsquo 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |