An SFA-HMM performance evaluation method using state difference optimization for running gear systems in high-speed trains

Cheng, Chao ; Wang, Meng ; Wang, Jiuhe ; Shao, Junjie ; Chen, Hongtian

International Journal of Applied Mathematics and Computer Science, Tome 32 (2022) no. 3, pp. 389-402.

Résumé

The evaluation of system performance plays an increasingly important role in the reliability analysis of cyber-physical systems. Factors of external instability affect the evaluation results in complex systems. Taking the running gear in high-speed trains as an example, its complex operating environment is the most critical factor affecting the performance evaluation design. In order to optimize the evaluation while improving accuracy, this paper develops a performance evaluation method based on slow feature analysis and a hidden Markov model (SFA-HMM). The utilization of SFA can screen out the slowest features as HMM inputs, based on which a new HMM is established for performance evaluation of running gear systems. In addition to directly classical performance evaluation for running gear systems of high-speed trains, the slow feature statistic is proposed to detect the difference in the system state through test data, and then eliminate the error evaluation of the HMM in the stable state. In addition, indicator planning and status classification of the data are performed through historical information and expert knowledge. Finally, a case study of the running gear system in high-speed trains is discussed. After comparison, the result shows that the proposed method can enhance evaluation performance.

Détail

Keywords: slow feature analysis, SFA, performance evaluation, hidden Markov model, HMM, running gear system
Mots-clés : analiza cech, SFA, ocena efektywności, ukryty model Markowa, HMM, układ biegowy

Bibliographie

[1] [1] Bui, D., Tuan, T., Klempe, H., Pradhan, B. and Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural, Landslides 13(2): 361–378, DOI: 10.1007/s10346-015-0557-6.

[2] [2] Chen, H., Chen, Z., Chai, Z., Jiang, B. and Huang, B. (2021). A single-side neural network-aided canonical correlation analysis with applications to fault diagnosis, IEEE Transactions on Cybernetics 52(9): 9454–9466, DOI: 10.1109/TCYB.2021.3060766.

[3] [3] Chen, H. and Jiang, B. (2020). A review of fault detection and diagnosis for the traction system in high-speed trains, IEEE Transactions on Intelligent Transportation Systems 21(2): 450–465, DOI: 10.1109/TITS.2019.2897583.

[4] [4] Chen, H., Jiang, B., Ding, S. and Huang, B. (2022). Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives, IEEE Transactions on Intelligent Transportation Systems 23(3): 1700–1716, DOI: 10.1109/TITS.2020.3029946.

[5] [5] Chen, H., Jiang, B., Lu, N. and Chen, W. (2020). Data-Driven Detection and Diagnosis of Faults in Traction Systems of High-Speed Trains, Springer Nature, Berlin, DOI: 10.1007/978-3-030-46263-5.

[6] [6] Chen, H., Jiang, B., Lu, N. and Mao, Z. (2018). Deep PCA based real-time incipient fault detection and diagnosis methodology for electrical drive in high-speed trains, IEEE Transactions on Vehicular Technology 67(6): 4819–4830, DOI: 10.1109/TVT.2018.2818538.

[7] [7] Cheng, C., Wang, J., Chen, H., Chen, Z., Luo, H. and Xie, P. (2021). A review of intelligent fault diagnosis for high-speed trains: Qualitative approaches, Entropy 23(1): 1, DOI: 10.3390/e23010001.

[8] [8] Deng, X., Tian, X., Chen, S. and Harris, C. (2018). Nonlinear process fault diagnosis based on serial principal component analysis, IEEE Transactions on Neural Networks and Learning Systems 29(3): 560–572, DOI: 10.1109/TNNLS.2016.2635111.

[9] [9] Don, M. and Khan, F. (2019). Process fault prognosis using hidden Markov model-Bayesian networks hybrid model, Industrial and Engineering Chemistry Research 58(27): 12041–12053, DOI: 10.1021/acs.iecr.9b00524.

[10] [10] Jiang, Q., Yan, X., Yi, H. and Gao, F. (2020). Data-driven batch-end quality modeling and monitoring based on optimized sparse partial least squares, IEEE Transactions on Industrial Electronics 67(5): 4098–4107, DOI: 10.1109/TIE.2019.2922941.

[11] [11] Jiang, Y. and Yin, S. (2019). Recent advances in key-performance-indicator oriented prognosis and diagnosis with a Matlab toolbox: DB-kit, IEEE Transactions on Industrial Informatics 15(5): 2849–2858, DOI: 10.1109/TII.2018.2875067.

[12] [12] Kaczorek, T. and Ruszewski, A. (2022). Global stability of discrete-time feedback nonlinear systems with descriptor positive linear parts and interval state matrices, International Journal of Applied Mathematics and Computer Science 32(1): 5–10, DOI: 10.34768/amcs-2022-0001.

[13] [13] Kiranyaz, S., Gastli, A., Ben-Brahim, L., Alemadi, N. and Gabbouj, M. (2018). Real-time fault detection and identification for MMC using 1D convolutional neural networks, IEEE Transactions on Industrial Electronics 66(11): 8760–8771, DOI: 10.1109/TIE.2018.2833045.

[14] [14] Li, S., Cao, H. and Yang, Y. (2018). Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification, Journal of Power Sources 378(99): 646–659, DOI: 10.1016/j.jpowsour.2018.01.015.

[15] [15] Liu, J., Shi, L., Yong, J. and Krishnamurthy, M. (2013a). Reliability evaluating for traction drive system of high-speed electrical multiple units, 2013 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, USA, DOI: 10.1109/ITEC.2013.6574491.

[16] [16] Liu, Y., Wang, F. and Chang, Y. (2013b). Online fuzzy assessment of operating performance and cause identification of nonoptimal grades for industrial processes, Industrial and Engineering Chemistry Research 52(50): 18022–18030, DOI: 10.1021/ie402243s.

[17] [17] Luo, H., Yin, S., Liu, T. and Khan, A. (2020). A data-driven realization of the control-performance-oriented process monitoring system, IEEE Transactions on Industrial Electronics 67(1): 521–530, DOI: 10.1109/TIE.2019.2892705.

[18] [18] Luo, H., Zhao, H. and Yin, S. (2018). Data-driven design of fog computing aided process monitoring system for large-scale industrial processes, IEEE Transactions on Industrial Informatics 14(10): 4631–4641, DOI: 10.1109/TII.2018.2843124.

[19] [19] Molaei, M., Oraee, H. and Fotuhi-Firuzabad,M. (2007). Markov model of drive-motor systems for reliability calculation, IEEE International Symposium on Industrial Electronics, Montreal, Canada, pp. 2286–2291.

[20] [20] Salazar, J.C., Sanjuan, A., Nejjari, F. and Sarrate, R. (2020). Health-aware and fault-tolerant control of an octorotor UAV system based on actuator reliability, International Journal of Applied Mathematics and Computer Science 30(1): 47–59, DOI: 10.34768/amcs-2020-0004.

[21] [21] Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. and Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis, Aiche Journal 61(11): 3666–3682, DOI: 10.1002/aic.14888.

[22] [22] Song, Y., Liu, Z., Rnnquist, A., Nvik, P. and Liu, Z. (2020). Contact wire irregularity stochastics and effect on high-speed railway pantograph–catenary interactions, IEEE Transactions on Instrumentation and Measurement 69(10): 8196–8206, DOI: 10.1109/TIM.2020.2987457.

[23] [23] Song, Y., Wang, Z., Liu, Z. and Wang, R. (2021). A spatial coupling model to study dynamic performance of pantograph-catenary with vehicle-track excitation, Mechanical Systems and Signal Processing 151: 107336, DOI: 10.1016/j.ymssp.2020.107336.

[24] [24] Sun, Q., Zhou, Y. and Li, M. (2020). Bearing operating state evaluation based on improved HMM, International Journal of Pattern Recognition and Artificial Intelligence 34(6), DOI: 10.1142/S0218001420590168.

[25] [25] Wang, S., Stroe, D., Fernandez, C., Xiong, L., Fan, Y. and Cao, W. (2020). A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model, Journal of Power Sources 242(5): 118506.1–118506.13, DOI: 10.1016/j.jclepro.2019.118506.

[26] [26] Wang, W., Xi, J., Chong, A. and Lin, L. (2017). Driving style classification using a semi-supervised support vector machine, Knowledge-Based Systems 47(5): 650–660, DOI: 10.1109/THMS.2017.2736948.

[27] [27] Wu, C., Du, B., Cui, X. and Zhang, L. (2017). A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion, Remote Sensing of Environment 199: 241–255, DOI: 10.1016/j.rse.2017.07.009.

[28] [28] Yan, A., Yu, H. and Wang, D. (2017). Case-based reasoning classifier based on learning pseudo metric retrieval, Expert Systems with Applications 89: 91–98, DOI: 10.1016/j.eswa.2017.07.022.

[29] [29] Yan, L., Dong, H. and Jia, L. (2015). A method on the evaluation technology of high speed railway infrastructure safety state, 2015 27th Chinese Control and Decision Conference (CCDC), Qingdao, China, DOI: 10.1109/CCDC.2015.7162506.

[30] [30] Yuan, X., Zhou, J., Huang, B., Wang, Y., Yang, C. and Gui, W. (2020). Hierarchical quality-relevant feature representation for soft sensor modeling: A novel deep learning strategy, IEEE Transactions on Industrial Informatics 16(6): 3721–3730, DOI: 10.1109/TII.2019.2938890.

[31] [31] Yun, T., Yong, Q., Yong, F., Zheng, J. and Jia, L. (2017). Reliability data analysis of bogie components of high speed train, Prognostics and System Health Management Conference, Chengdu, China, DOI: 10.1109/PHM.2016.7819892.

[32] [32] Zhang, F., Zhang, Z., Zhang, P. and Wang, S. (2018). UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering, Knowledge-Based Systems 148: 146–166, DOI: 10.1016/j.knosys.2018.02.032.

[33] [33] Zhang, M., Wan, X., Gang, L., Lv, X., Wu, Z. and Liu, Z. (2021). An automated driving strategy generating method based on WGAIL–DDPG, International Journal of Applied Mathematics and Computer Science 31(3): 461–470, DOI: 10.34768/amcs-2021-0031.

[34] [34] Zhang, S. and Zhao, C. (2019). Slow-feature-analysis-based batch process monitoring with comprehensive interpretation of operation condition deviation and dynamic anomaly, IEEE Transactions on Industrial Electronics 66(5): 3773–3783, DOI: 10.1109/TIE.2018.2853603.

[35] [35] Zheng, Y., Zhao, F. and Wang, Z. (2019). Fault diagnosis system of bridge crane equipment based on fault tree and Bayesian network, International Journal of Advanced Manufacturing Technology 105(9): 3605–3618, DOI: 10.1007/s00170-019-03793-0.

[36] [36] Zou, X. and Zhao, C. (2019). Meticulous assessment of operating performance for processes with a hybrid of stationary and nonstationary variables, Industrial and Engineering Chemistry Research 58(3): 1341–1351, DOI: 10.1021/acs.iecr.8b05005.