Abstract
Aimed at the CAN technology utilized on automobile currently, and the complexity of the automobile fault information and the difficulty of diagnosis, CAN bus adapter is designed .Microsoft Visual C++ 6.0 is utilized to build the Kalman digital filter and automobile fault diagnosis system based on BP network. Incepting the signal from the CAN bus, filtering and removal of noise, and online fault diagnosis and forecast to the main systems of automobile. Experiments show that Kalman filtering plays good on removal of noise from the automobile fault signals, and the BP network trainings of the systems are effective to implement non-linear mapping from the fault phenomenon of automobile to the fault position.
Supported by the Key Project of Chinese Ministry of Education (No.: 208037); Scientific Research Fund of Heilongjiang Provincial Education Department (No.:11551072).
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
Preview
Unable to display preview. Download preview PDF.
References
Chen, Y., Hu, Y.: Failure diagnose research for engine of basic BP NN. CAT (1), 81–83 (2008)
GmbH, R.B.: BOSCH’S Control 1er Area Network [EB/OL], http://www.can.bosch.com
Szabo, S., Oplustil, v.: Distributed CAN based control system for robotic and airbormapplications. J. IEEE on Control, Automation, Robotics and Vision 3(11), 1233–1238 (2007)
Haykin, S.: Adaptive Filter Theory, 4th edn., vol. 11, pp. 369–402. Publishing House of Electronics Industry (2006)
Zhang, X.D., Lu, M.: Research on neural network integration fusion method and application on the fault diagnosis of automotive engine. In: 2007 2nd IEEE IEA, pp. 480–483 (2007)
Wang, S.W., Yu, D.L.: Neural network in stable adaptive control law for automotive engines. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4491, pp. 122–131. Springer, Heidelberg (2007)
Yang, D.Z., Wang, J.: Estimation and control of hybrid electric vehicle using artificial neural networks. In: 2007 2nd IEEE IEA, pp. 35–40 (2007)
Chen, X.J., Gao, Z.F.: Applications of ANNs in geotechnical engineering. In: EI CEMI, Papers 3, pp. 656–659 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, M., Ao, X., Wang, J. (2011). Fault Diagnosis of Automobile Based on CAN Bus. In: Qi, L. (eds) Information and Automation. ISIA 2010. Communications in Computer and Information Science, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19853-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-19853-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19852-6
Online ISBN: 978-3-642-19853-3
eBook Packages: Computer ScienceComputer Science (R0)