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Automotive Engineering ›› 2023, Vol. 45 ›› Issue (4): 699-708.doi: 10.19562/j.chinasae.qcgc.2023.04.019

Special Issue: 底盘&动力学&整车性能专题2023年

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Analysis of Vehicle Diagnostic Trouble Codes Based on Association Rule Mining

Jie Hu1(),Hao Geng1,Yuanjie Li1,Huangzheng Geng2,Minmin Tong2   

  1. 1.Wuhan University of Technology,Hubei Key Laboratory of Modern Auto Parts Technology,Hubei Collaborative Innovation Center for Automotive Components Technology,Hubei Research Center for New Energy & Intelligent Connected Vehicle,Wuhan  430070
    2.SAIC General Wuling Automobile Co. ,Ltd. ,Liuzhou  545007
  • Received:2022-09-02 Revised:2022-10-18 Online:2023-04-25 Published:2023-04-19
  • Contact: Jie Hu E-mail:auto_hj@163. com

Abstract:

Based on the principle of on-board diagnosis, this paper combines analysis of diagnostic trouble codes (DTCs) generated and saved during vehicle self-diagnosis with association rule mining, proposes an improved FP-tree algorithm suitable for exploring DTCs data association, and establishes the vehicle DTCs association diagram according to the obtained association rule, which is applied to the historical data analysis process and vehicle maintenance process. Interesting association rules in data is explored to provide association visualization results. The miscellaneous DTCs read in the maintenance process is analyzed to reduce the complexity of the DTCs and the main DTCs are analyzed to shorten the maintenance time based on the DTCs and help the maintenance personnel to locate the faults.

Key words: diagnostic trouble codes, association rule, frequent pattern tree, main DTCs analysis