Volume 23 Issue 6
Dec.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(6): 216-231
Next Previous
YANG Biao, MEI Zi, LONG Zhi-qiang. Online anomaly detection method integrating LSTM and MGD for suspension system of maglev trains[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 216-231. doi: 10.19818/j.cnki.1671-1637.2023.06.014
Citation: YANG Biao, MEI Zi, LONG Zhi-qiang. Online anomaly detection method integrating LSTM and MGD for suspension system of maglev trains[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 216-231. doi: 10.19818/j.cnki.1671-1637.2023.06.014
shu

Online anomaly detection method integrating LSTM and MGD for suspension system of maglev trains

doi: 10.19818/j.cnki.1671-1637.2023.06.014

  • College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, Hunan, China

Funds:

National Natural Science Foundation of China 52232013

National Natural Science Foundation of China 52332011

More Information
    Abstract
  • In view of online anomaly detection of suspension system of maglev trains, a detection method based on long short-time memory (LSTM) neural network and multivariate Gaussian distribution (MGD) was proposed. The LSTM time series prediction model for learning the normal operation of the suspension system was established, and the prediction errors under normal conditions were obtained. Based on the prediction errors of the gap, current, and acceleration, the MGD model reflecting the distribution characteristics of the prediction errors under normal conditions was built. The log probability density was used as the detection indicator, the online detection logic was designed, and the threshold was set using F1 score as the evaluation criterion of the detection effect. To verify the effectiveness of the proposed method, the operation line data of the maglev train were used to simulate online data, and the proposed method was applied to detect and analyze track crossing joint, rail smashing, and acceleration signal anomalies. Research results show that the F1 scores of the proposed method for detecting the above three anomalies are 100.00%, 97.85%, and 83.33%, respectively. The detection indicators of the proposed method are significantly different between normal and abnormal conditions, reflecting the specific time period from anomaly occurrence of the suspension system to normal adjustment, and the algorithm takes about 2 s on average. Compared with the method based on the Gaussian distribution of hyperspheres, the proposed method achieves an average improvement of 1.9% in detection rate. Specifically, it achieves a 9.4% increase in detecting short-duration track crossing joint anomaly. Therefore, the proposed method can achieve online anomaly detection of the suspension system's state data.

     

    • FullText(HTML)
  • loading
    • References(33)
  • [1]
    MA Wei-hua, LUO Shi-hui, ZHANG Min, et al. Research review on medium and low speed maglev vehicle[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 199-216. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.01.009
    [2]
    DENG Zi-gang, LIU Zong-xin, LI Hai-tao, et al. Development status and prospect of maglev train[J]. Journal of Southwest Jiaotong University, 2022, 57(3): 455-574, 530. (in Chinese)
    [3]
    XU Fei, LUO Shi-hui, DENG Zi-gang. Study on key technologies and whole speed range application of maglev rail transport[J]. Journal of the China Railway Society, 2019, 41(3): 40-49. (in Chinese) doi: 10.3969/j.issn.1001-8360.2019.03.006
    [4]
    LIANG Xiao, CHEN Feng, FU Qing-xiang. Key technical issues on 160 km·h-1 medium-speed maglev transit system[J]. Urban Mass Transit, 2019, 22(9): 21-26. (in Chinese)
    [5]
    NASIRI Z R, HEKMATI A. A review of suspension and traction technologies in maglev trains[C]//IEEE. 34th International Power System Conference(PSC). New York: IEEE, 2019: 129-135.
    [6]
    PHAENKONGNGAM T, CHINNAWONG K, PATUMASUIT N, et al. Reviewing propulsion and levitation system for magnetic levitation train[C]//IEEE. 2021 International Electrical Engineering Congress. New York: IEEE, 2021: 185-188.
    [7]
    WANG Ping, MEI Zi, LONG Zhi-qiang. Anomaly detection for suspension systems based on the Gaussian distribution of hyperspheres[J]. Electric Drive for Locomotives, 2021(6): 9-17. (in Chinese)
    [8]
    WANG Ping, MEI Zi, LONG Zhi-qiang. Anomaly detection method of middle-low speed suspension system based on improved canonical correlation analysis[J]. Journal of Tongji University (Natural Science), 2022, 50(2): 241-252. (in Chinese)
    [9]
    LUO Jian-hui, WANG Ping. Anomaly detection method of middle-low speed suspension system based on Hellinger distance and correlation coefficient[J]. Journal of Railway Science and Engineering, 2022, 19(10): 3096-3106. (in Chinese)
    [10]
    ZHANG Yun-zhou, CHAO Chuang, WU Jun, et al. Magnetic anomaly of long track detection method based on wavelet combining with fractal for high speed maglev transit[J]. Measurement and Control, 2022, 55(7/8): 717-728.
    [11]
    ZHOU Xu, WEN Tao, LONG Zhi-qiang. Anomaly detection of suspension system in maglev train based on missed detection rate[J]. Journal of Southwest Jiaotong University, 2023, 58(4): 903-912. (in Chinese)
    [12]
    LONG Zi-fan, SI En, LUO Hua-jun, et al. A method for monitoring and diagnosing abnormal vibration of maglev train[J]. Scientific and Technological Innovation, 2022(10): 21-24. (in Chinese)
    [13]
    WU Jun, LI Hong-lu, ZHANG Yu-xin, et al. Screening method of suspected irregularity for medium-and-low-speed maglev tracks based on Box-Whisker plot[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 68-76. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2023.03.004
    [14]
    CHEN Po-yu, YANG Shu-sen, MCCANN J A. Distributed real-time anomaly detection in networked industrial sensing systems[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3832-3842. doi: 10.1109/TIE.2014.2350451
    [15]
    AHMAD S, LAVIN A, PURDY S, et al. Unsupervised real-time anomaly detection for streaming data[J]. Neurocomputing, 2017, 262: 134-147. doi: 10.1016/j.neucom.2017.04.070
    [16]
    XIE Kun, LI Xiao-can, WANG Xin, et al. On-line anomaly detection with high accuracy[J]. IEEE/ACM Transactions on Networking, 2018, 26(3): 1222-1235. doi: 10.1109/TNET.2018.2819507
    [17]
    LUO Jian, HONG Tao, YUE Meng. Real-time anomaly detection for very short-term load forecasting[J]. Journal of Modern Power Systems and Clean Energy, 2018, 6(2): 235-243. doi: 10.1007/s40565-017-0351-7
    [18]
    FISCH A T M, BARDWELL L, ECKLEY I A. Real time anomaly detection and categorisation[J]. Statistics and Computing, 2022, 32(4): 55. doi: 10.1007/s11222-022-10112-3
    [19]
    ZHOU Yan-jun, REN Huo-rong, LI Zhi-wu, et al. Anomaly detection based on a granular Markov model[J]. Expert Systems with Applications, 2022, 187: 115744. doi: 10.1016/j.eswa.2021.115744
    [20]
    LIU Sheng-hua, ZHOU Bin, DING Quan, et al. Time series anomaly detection with adversarial reconstruction networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(4): 4293-4306. doi: 10.1109/TKDE.2021.3140058
    [21]
    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
    [22]
    CHEN Xing-shu, JIANG Tian-yu, ZENG Xue-mei, et al. Network anomaly detector based on multiple time series analysis[J]. Advanced Engineering Sciences, 2017, 49(1): 144-150. (in Chinese)
    [23]
    LI Xin-peng, GAO Xin, YAN Bo, et al. An approach of data anomaly detection in power dispatching streaming data based on isolation forest algorithm[J]. Power System Technology, 2019, 43(4): 1447-1456. (in Chinese)
    [24]
    SHI Lei. Anomaly detection algorithm in streaming data based on LSTMs-Autoencoder[J]. Instrument Technique and Sensor, 2021(10): 120-125. (in Chinese) doi: 10.3969/j.issn.1002-1841.2021.10.024
    [25]
    MAO Wen-tao, TIAN Si-yu, DOU Zhi, et al. A new deep transfer learning-based online detection method of rolling bearing early fault[J]. Acta Automatica Sinica, 2022, 48(1): 302-314. (in Chinese)
    [26]
    CHEN Zhong-lei, YI Peng, CHEN Xiang, et al. Real-time anomaly detection framework via system calls based on integrated learning[J]. Computer Engineering, 2023, 49(6): 162-169, 179. (in Chinese)
    [27]
    GUE I H V, UBANDO A T, TSENG M, et al. Artificial neural networks for sustainable development: a critical review[J]. Clean Technologies and Environmental Policy, 2020, 22: 1449-1465. doi: 10.1007/s10098-020-01883-2
    [28]
    SHARMA S, SHARMA S, ATHAIYA A. Activation functions in neural networks[J]. International Journal of Engineering Applied Sciences and Technology, 2020, 4(12): 310-316.
    [29]
    SALEHINEJAD H, SANKAR S, BARFETT J, et al. Recent advances in recurrent neural networks[J]. arXiv, 2017, DOI: 10.48550/arXiv.1801.01078.
    [30]
    YU Yong, SI Xiao-sheng, HU Chang-hua, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Computation, 2019, 31(7): 1235-1270. doi: 10.1162/neco_a_01199
    [31]
    LIU Dian-yu, SUN Chuan-le, GAO Jun. Machine learning of log-likelihood functions in global analysis of parton distributions[J]. arXiv, 2022, DOI: 10.48550/arXiv.2201.06586.
    [32]
    DING Mei-mei, TIAN Hui. PCA-based network traffic anomaly detection[J]. Tsinghua Science and Technology, 2016, 21(5): 500-509.
    [33]
    YANG Min, ZHANG Huan-guo, FU Jian-ming, et al. Anomaly intrusion detection method based on SVDD[J]. Computer Engineering, 2005, 31(3): 39-42. (in Chinese)
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1581) PDF downloads(156) Cited by()
    Related

    Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@gfscw.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return