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 |
[1] |
马卫华, 罗世辉, 张敏, 等. 中低速磁浮车辆研究综述[J]. 交通运输工程学报, 2021, 21(1): 199-216. doi: 10.19818/j.cnki.1671-1637.2021.01.009
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] |
邓自刚, 刘宗鑫, 李海涛, 等. 磁悬浮列车发展现状与展望[J]. 西南交通大学学报, 2022, 57(3): 455-474, 530.
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] |
徐飞, 罗世辉, 邓自刚. 磁悬浮轨道交通关键技术及全速度域应用研究[J]. 铁道学报, 2019, 41(3): 40-49. doi: 10.3969/j.issn.1001-8360.2019.03.006
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] |
梁潇, 陈峰, 傅庆湘. 160 km·h-1中速磁浮交通系统的关键技术问题[J]. 城市轨道交通研究, 2019, 22(9): 21-26.
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] |
王平, 梅子, 龙志强. 基于超球体高斯分布的悬浮系统异常检测[J]. 机车电传动, 2021(6): 9-17.
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] |
王平, 梅子, 龙志强. 基于改进典型相关分析的中低速悬浮系统异常检测方法[J]. 同济大学学报(自然科学版), 2022, 50(2): 241-252.
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] |
罗建辉, 王平. 基于海林格距离和相关系数的中低速悬浮系统异常检测方法[J]. 铁道科学与工程学报, 2022, 19(10): 3096-3106.
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] |
周旭, 温韬, 龙志强. 基于漏检率的磁浮列车悬浮系统异常检测[J]. 西南交通大学学报, 2023, 58(4): 903-912.
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] |
龙子凡, 司恩, 罗华军, 等. 一种磁浮列车异常振动监测与诊断方法[J]. 科学技术创新, 2022(10): 21-24.
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] |
吴峻, 李洪鲁, 张雨馨, 等. 中低速磁浮轨道疑似不平顺的Box-Whisker图筛选法[J]. 交通运输工程学报, 2023, 23(3): 68-76. doi: 10.19818/j.cnki.1671-1637.2023.03.004
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] |
王德文, 杨力平. 智能电网大数据流式处理方法与状态监测异常检测[J]. 电力系统自动化, 2016, 40(14): 122-128. doi: 10.7500/AEPS20150828001
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] |
陈兴蜀, 江天宇, 曾雪梅, 等. 基于多维时间序列分析的网络异常检测[J]. 工程科学与技术, 2017, 49(1): 144-150.
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] |
李新鹏, 高欣, 阎博, 等. 基于孤立森林算法的电力调度流数据异常检测方法[J]. 电网技术, 2019, 43(4): 1447-1456.
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] |
时磊. 基于LSTMs-Autoencoder的流数据异常检测算法[J]. 仪表技术与传感器, 2021(10): 120-125. doi: 10.3969/j.issn.1002-1841.2021.10.024
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] |
毛文涛, 田思雨, 窦智, 等. 一种基于深度迁移学习的滚动轴承早期故障在线检测方法[J]. 自动化学报, 2022, 48(1): 302-314.
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] |
陈仲磊, 伊鹏, 陈祥, 等. 基于集成学习的系统调用实时异常检测框架[J]. 计算机工程, 2023, 49(6): 162-169, 179.
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] |
杨敏, 张焕国, 傅建明, 等. 基于支持向量数据描述的异常检测方法[J]. 计算机工程, 2005, 31(3): 39-42.
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)
|