Paper Title:
Fault Diagnosis of Automobile ECUs with Data Mining Technologies
  Abstract

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

  Info
Periodical
Edited by
Zhu Zhilin & Patrick Wang
Pages
156-161
DOI
10.4028/www.scientific.net/AMM.40-41.156
Citation
Y. Li, Y. Q. Li, Z. X. Wang, "Fault Diagnosis of Automobile ECUs with Data Mining Technologies", Applied Mechanics and Materials, Vols. 40-41, pp. 156-161, 2011
Online since
November 2010
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Price
$32.00
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