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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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    • References(32)
  • [1]
    ZHAO Ying-xun. Detection and analysis of brake force based on roller opposite-force brake tester[J]. China Measurement and Test, 2015, 41 (4): 10-13. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SYCS201504003.htm
    [2]
    MA Wen-yao, WU Zhao-lin, LI Wei-feng. Conformal detection algorithm of anomalous behaviors of vessel[J]. Journal of Traffic and Transportation Engineering, 2017, 17 (5): 149-158. (in Chinese). doi: 10.3969/j.issn.1671-1637.2017.05.014
    [3]
    ZHOU Dong-hua, WEI Mu-heng, SI Xiao-sheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes[J]. Acta Automatica Sinica, 2013, 39 (6): 711-722. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201306005.htm
    [4]
    YAO Xin-xin, LIU Ying-bo, ZHAO Jiong, et al. Device group-oriented method for abnormal floor data detecting[J]. Computer Integrated Manufacturing Systems, 2013, 19 (12): 2993-3001. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ201312010.htm
    [5]
    YAN Ying-jie, SHENG Ge-hao, CHEN Yu-feng, et al. An method for anomaly detection of state information of power equipment based on big data analysis[J]. Proceedings of the CSEE, 2015, 35 (1): 52-59. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201501008.htm
    [6]
    O Tao, ZHOU Xi, MA Bo, et al. Detecting abnormal time series data of gas station utilizing Seq2Seq model based on bi-LSTM[J]. Journal of Computer Applications, 2018: 1-7, DOI: 10.11772/j.issn.1001-9081.2018081681.(in Chinese).
    [7]
    SHI Bin, JIANG Ji-ping, WANG Peng. Early warning of water pollution incidents based on abnormal change of water quality data from high frequency online monitoring[J]. China Environmental Science, 2017, 37 (11): 4394-4400. (in Chinese). doi: 10.3969/j.issn.1000-6923.2017.11.046
    [8]
    BAO Yue-quan, TANG Zhi-yi, LI Hui, et al. Computer vision and deep learning-based data anomaly detection method for structural health monitoring[J]. Structural Health Monitoring, 2018: 1-21, DOI: 10.1177/1475921718757405.
    [9]
    SHANG Wen-li, ZHANG Sheng-shan, WAN Ming, et al. Modbus/TCP communication anomaly detection algorithm based on PSO-SVM[J]. Acta Electronica Sinica, 2014, 42 (11): 2314-2320. (in Chinese). doi: 10.3969/j.issn.0372-2112.2014.11.029
    [10]
    WANG De-wen, YANG Li-ping. Stream processing method and condition monitoring anomaly detection for big data in smart grid[J]. Automation of Electric Power Systems, 2016, 40 (14): 122-128. (in Chinese). doi: 10.7500/AEPS20150828001
    [11]
    YU Yu-feng, ZHU Yue-long, WAN Ding-sheng, et al. Time series outlier detection based on sliding window prediction[J]. Journal of Computer Applications, 2014, 34 (8): 2217-2220, 2226. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201408018.htm
    [12]
    ZHANG Zhong-lin, ZHOU Xiao-xia. Method of mining sequential association rules based on clustering of sliding window[J]. Computer Engineering and Design, 2014, 35 (4): 1408-1413. (in Chinese). doi: 10.3969/j.issn.1000-7024.2014.04.055
    [13]
    LIU Zi-hao, LI Ling, YE Feng. Anomaly detection method for hydrologic sensor data based on SparkR[J]. Journal of Computer Applications, 2018: 1-6, DOI:10.11772/j.issn.1001-9081.2018081782.(in Chinese).
    [14]
    LI Hai-lin, WU Xian-li. Time series anomaly detection method based on frequent pattern discovery[J]. Journal of Computer Applications, 2018, 38 (11): 3204-3210. (in Chinese). doi: 10.11772/j.issn.1001-9081.2018041252
    [15]
    LI Guang, WANG Jie, LIANG Jing, et al. Application of sliding nest window control chart in data stream anomaly detection[J]. Symmetry, 2018, 10 (4): 1-13.
    [16]
    FEI Huan, XIAO Fu, LI Guang-hui, et al. An anomaly detection method of wireless sensor network based on multimodals data stream[J]. Chinese Journal of Computers, 2017, 40 (8): 1829-1842. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJX201708007.htm
    [17]
    DING Zhi-guo, FEI Min-rui. An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window[J]. IFAC Proceedings Volumes, 2013, 46 (20): 12-17. doi: 10.3182/20130902-3-CN-3020.00044
    [18]
    WANG Gui-lan, ZHOU Guo-liang, ZHAO Hong-shan, et al. Fast clustering and anomaly detection technique for large-scale power data stream[J]. Automation of Electric Power Systems, 2016, 40 (24): 27-33. (in Chinese). doi: 10.7500/AEPS20160123002
    [19]
    AGHABOZORGI S, SHIRKHORSHIDI A S, WAH T Y. Time-series clustering-a decade review[J]. Information Systems, 2015, 53: 16-38. doi: 10.1016/j.is.2015.04.007
    [20]
    FAHAD A, ALSHATRI N, TARI Z, et al. A survey of clustering algorithms for big data: taxonomy and empirical analysis[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2 (3): 267-279. doi: 10.1109/TETC.2014.2330519
    [21]
    FAROUGHI A, JAVIDAN R. CANF: Clustering and anomaly detection method using nearest and farthest neighbor[J]. Future Generation Computer Systems, 2018, 89: 166-177. doi: 10.1016/j.future.2018.06.031
    [22]
    JAIN B J. The mean partition theorem in consensus clustering[J]. Pattern Recognition, 2018, 79: 427-439. doi: 10.1016/j.patcog.2018.01.030
    [23]
    ISMKHAN H. I-k-means-plus: An iterative clustering algorithm based on an enhanced version of the K-means[J]. Pattern Recognition, 2018, 79: 402-413. doi: 10.1016/j.patcog.2018.02.015
    [24]
    RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344: 1492-1496. doi: 10.1126/science.1242072
    [25]
    TIAN Li, XIANG Min. Abnormal power consumption analysis based on density-based spatial clustering of applications with noise in power system[J]. Automation of Electric Power Systems, 2017, 41 (5): 64-70. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201705010.htm
    [26]
    ZHUANG Chi-jie, ZHANG Bin, HU Jun, et al. Anomaly detection for power consumption patterns based on unsupervised learning[J]. Proceedings of the CSEE, 2016, 36 (2): 379-387. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201602008.htm
    [27]
    PURARJOMANDLANGRUDI A, GHAPANCHI A H, ESMALIFALAK M. A data mining approach for fault diagnosis: an application of anomaly detection algorithm[J]. Measurement, 2014, 55: 343-352. doi: 10.1016/j.measurement.2014.05.029
    [28]
    RANSHOUS S, SHEN S, KOUTRA D, et al. Anomaly detection in dynamic networks: a survey[R]. Hoboken: Wiley Periodicals, Inc., 2015.
    [29]
    THEISSLER A. Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection[J]. KnowledgeBased Systems, 2017, 123: 163-173.
    [30]
    LI Guang-hui, LU Wen-wei, FENG Hai-lin. Outlier detection methods based on neural network in wireless sensor networks[J]. Computer Science, 2014, 41 (11A): 208-211.
    [31]
    PANDEESWARI N, KUMAR G. Anomaly detection system in cloud environment using fuzzy clustering based ANN[J]. Mobile Networks and Applications, 2016, 21: 494-505.
    [32]
    WU Jia, ZENG Wei-ru, YAN Fei. Hierarchical temporal memory method for time-series-based anomaly detection[J]. Neurocomputing, 2018, 273: 535-546.
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