Abstract
The automotive ECUs is becoming more and more complicated, and so is the fault diagnosis. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on fault database. By making full use of data stream, we firstly extract symptom vector by processing data steam and pre-processing rules, and then we use the symptom vector to match the fault pattern in fault database, we use the unmatched vector as the test case of C4.5 decision tree algorithm to create the link rules between fault symptom and fault reason, and finally store the rules into the fault database. An example of ETCs is showed to testify the fault diagnosis method. The test result confirm the reliability and validity of this diagnosis method.
F2012-D02-026
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ya D (2009) Data analysis in automotive fault diagnosis. Machinery Industry Press, Beijing, pp 2–4
Yi J (2011) Research on fault diagnosis expert system of automotive engine based on ontology. Electrical Control Eng (ICECE), pp 5409–5412
Choi K (2006) Data reduction techniques for intelligent fault diagnosis in automotive systems. Autotestcon, pp 66–72
Namburu SM (2006) Application of signal analysis and data-driven approaches to fault detection and diagnosis in automotive engines. Systems, Man and ybernetics, pp 3665–3670
Stein B (2003) Model compilation and diagnosability of technical systems. In: Hanza MH (ed) Proceeding of the 3rd IASTED international conference on artificial intelligence and application (AIA 03), BenalmAqdena, Spain, pp 191–197, ACTA Press, Sept 2003
Xuesen Q (2005) A new discipline of science-The study of open complex giant system and its methnology. Urban Stud 12(5):1–8
Technical support to the national highway traffic safety administration on the reported toyota motor corporation unintended acceleration investigation. 2011
Li Y, Li Y (2010) Fault diagnosis of automobile ECUs with data mining technologies. Adv Sci Eng 2010:156–161
Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers, Burlington
Hui O, lebin L (2010) Research of paper metadata extraction algorithm based on C4.5. Comput Eng Des 2010(16):3708–3711
Acknowledgments
This work has been supported by Natural Science Foundation of Shandong Province, China (ZR2011FQ034).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Li, Y., Wang, Z., Zhuang, R., Li, J. (2013). Automotive ECUs Fault Diagnosis Modeling Based on the Fault Database. In: Proceedings of the FISITA 2012 World Automotive Congress. Lecture Notes in Electrical Engineering, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33829-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-33829-8_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33828-1
Online ISBN: 978-3-642-33829-8
eBook Packages: EngineeringEngineering (R0)