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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(6): 82-92
NING Hang, NAN Chun-li, YANG Lan, ZHAO Xiang-mo, LIU Hao-xue, ZHOU Dan. Anomaly detection of automobile braking curves[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 82-92. doi: 10.19818/j.cnki.1671-1637.2018.06.009
Citation: NING Hang, NAN Chun-li, YANG Lan, ZHAO Xiang-mo, LIU Hao-xue, ZHOU Dan. Anomaly detection of automobile braking curves[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 82-92. doi: 10.19818/j.cnki.1671-1637.2018.06.009
shu

Anomaly detection of automobile braking curves

doi: 10.19818/j.cnki.1671-1637.2018.06.009
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

More Information
  • Author Bio:

    NING Hang(1975-), male, lecturer, doctoralstudent, ninghang@chd.edu.cn

    ZHAO Xiang-mo(1966-), male, professor, PhD, xmzhao@chd.edu.cn

  • Received Date: 2018-09-26
  • Publish Date: 2018-12-25
    Abstract
  • The anomaly detection methods of automobile braking curves were studied.The features of the depressing-releasing-depressing anomaly were analyzed.The detection conditions and changing trends of the braking curves were considered.The braking data were clustered based on the cosine similarity and relative error.The segmentation algorithm of the braking curves was established.The braking curves were divided into blocking segment, rising segment, continuous segment, and releasing segment, and the corresponding data subsets were extracted.9 120 braking force curves from 3 inspection institutions were manually screened and analyzed, the three anomalous features including braking advance, depressing-releasing-depressing and braking growth lag were summarized, and the corresponding anomaly detection algorithms were given. For more difficult to identify the depressing-releasing-depressing anomaly, the subsequence with the longest continuous and descending trend in the ascending segment was searched according to the dynamic programming idea.The distance ratio of the subsequence in theascending segment was calculated, and compared with the corresponding experiential value to determine whether the subsequence was anomalous or not.Research result shows that the lowdimensional braking data can be clustered into blocking segment, rising segment, continuous segment, and releasing segment according to the cosine similarity only when the dimensions are not higher than 32.For the high-dimensional data, because their dimensions are higher than 32, the demarcation point has less influence on the overall sequence similarity.In this case, the relative error of the demarcation point must be combined with the similarity to constrain the clustering result for determining the blocking segment, rising segment, continuous segment, and releasing segment of the braking curve.Since the distance ratio of the depressing-releasingdepressing subsequence in the ascending segment is 9.8% and larger than the empirical value of 8.2%, the braking curve has depressing-releasing-depressing anomaly. This is correct, therefore, the method is reliable.

     

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