MATEC Web Conf.
Volume 252, 2019III International Conference of Computational Methods in Engineering Science (CMES’18)
|Number of page(s)||5|
|Section||Application of Computer Programs in Technology|
|Published online||14 January 2019|
Architecture of on-line data acquisition system for car on-board diagnostics
AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Applied Computer Science, al. A. Mickiewicza 30, Poland
* Corresponding author: firstname.lastname@example.org
Modern cars produced for the last two decades are full of electronic devices called Electronic Control Units (ECU). They are responsible for collecting diagnostic data from different components such as the engine, breaks etc. using probes and sensors. The collected data are validated against built-in heuristic and abnormal behaviour is reported to a driver by a gauge on an instrument cluster. ECUs use data provided by other ECUs. Information is transmitted over the dedicated network called Controlled Area Network (CAN). Every car equipped with ECUs and CAN exposes information over universal diagnostic interface called On-Board Diagnostic. Using the interface, it is possible to gather car's live data. With the data mining approach, it is possible to exploit the collected more effectively to obtain much more information about the functioning of car components than it is provided by standard vehicle equipment. The paper describes how to build a laboratory set to facilitate automated data collection. It consists of three major components: data acquisition, automated logs collection and persistent storage with presentation tools. The first component is based on Torque application for which reverse engineering was performed.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.