[1] 姜斌, 吴云凯, 陆宁云, 冒泽慧. 高速列车牵引系统故障诊断与预测技术综述. 控制与决策, 2018, 33(5): 841−855

1 Jiang Bin, Wu Yun-Kai, Lu Ning-Yun, Mao Ze-Hui. Review of fault diagnosis and prognosis techniques for high-speed railway traction system. Control and Decision, 2018, 33(5): 841−855
[2] 2 Shang J, Chen M Y, Ji H Q, Zhou D H. Recursive transformed component statistical analysis for incipient fault detection. Automatica, 2017, 80: 313−327 doi: 10.1016/j.automatica.2017.02.028
[3] 3 Lei Y G, Qiao Z J, Xu X F, Liu J, Niu S T. An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2017, 94: 148−164 doi: 10.1016/j.ymssp.2017.02.041
[4] 周东华, 纪洪泉, 何潇. 高速列车信息控制系统的故障诊断技术. 自动化学报, 2018, 44(7): 1153−1164

4 Zhou Dong-Hua, Ji Hong-Quan, He Xiao. Fault diagnosis techniques for the information control system of high-speed trains. Acta Automatica Sinica, 2018, 44(7): 1153−1164
[5] 5 Tobon-Mejia D A, Medjaher K, Zerhouni N, Tripot, G. A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Transactions on Reliability, 2012, 61(2): 491−503 doi: 10.1109/TR.2012.2194177
[6] 6 Haque M S, Choi S, Baek J. Auxiliary particle filtering-based estimation of remaining useful life of IGBT. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2693−2703 doi: 10.1109/TIE.2017.2740856
[7] 7 Tseng S T, Balakrishnan N, Tsai C C. Optimal step-stress accelerated degradation test plan for gamma degradation processes. IEEE Transactions on Reliability, 2009, 58(4): 611−618 doi: 10.1109/TR.2009.2033734
[8] 8 Zhai Q, Ye Z S. RUL prediction of deteriorating products using an adaptive wiener process model. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2911−2921 doi: 10.1109/TII.2017.2684821
[9] 9 Si X S, Wang W B, Hu C H, Zhou D H. Remaining useful life estimation-a review on the statistical data driven approaches. European Journal of Operational Research, 2011, 213(1): 1−14 doi: 10.1016/j.ejor.2010.11.018
[10] 10 Lu N Y, Yao Y, Gao F R. Two-dimensional dynamic PCA for batch process monitoring. AIChE Journal, 2005, 51(12): 3300−3304 doi: 10.1002/aic.10568
[11] 11 Zhao C H, Gao F R. Online fault prognosis with relative deviation analysis and vector autoregressive modeling. Chemical Engineering Science, 2015, 138(22): 531−543
[12] 12 Zhao C H, Gao F R. Critical-to-fault-degradation variable analysis and direction extraction for online fault prognostic. IEEE Transactions on Control Systems Technology, 2017, 25(3): 842−854 doi: 10.1109/TCST.2016.2576018
[13] 13 Li Y, Lu N Y, Wang X L, Jiang B. Islanding fault detection based on data-driven approach with active developed reactive power variation. Neurocomputing, 2019, 337(14): 97−109
[14] 14 Pham H T, Yang B S, Nguyen T T. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing, 2012, 32: 320−330 doi: 10.1016/j.ymssp.2012.02.015
[15] 15 Huang H Z, Wang H K, Li Y F, Zhang L L, Liu Z L. Support vector machine based estimation of remaining useful life: current research status and future trends. Journal of Mechanical Science and Technology, 2015, 29(1): 151−163 doi: 10.1007/s12206-014-1222-z
[16] 16 Tipping M E. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 2001, 1(3): 211−44
[17] 17 Yu H Y, Wu Z H, Chen D W, Ma X L. Probabilistic prediction of bus headway using relevance vector machine regression. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(7): 1772−1781 doi: 10.1109/TITS.2016.2620483
[18] 18 Wu Y M, Breaz E, Gao F, Miraoui A. A modified relevance vector machine for PEM fuel-cell stack aging prediction. IEEE Transactions on Industry Applications, 2016, 52(3): 2573−2581 doi: 10.1109/TIA.2016.2524402
[19] 19 Saha B, Goebel K, Poll S, Christophersen J. Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 2009, 58(2): 291−296 doi: 10.1109/TIM.2008.2005965
[20] 20 Widodo A, Kim, E Y, Son J D, Yang B S, Tan A C, Gu D S, Choid B K, Mathew J. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, 2009, 36(3): 7252−7261 doi: 10.1016/j.eswa.2008.09.033
[21] 21 Zio E, Di Maio F. Fatigue crack growth estimation by relevance vector machine. Expert Systems with Applications, 2012, 39(12): 10681−10692 doi: 10.1016/j.eswa.2012.02.199
[22] 22 Widodo A, Yang B S. Application of relevance vector machine and survival probability to machine degradation assessment. Expert Systems with Applications, 2011, 38(3): 2592−2599 doi: 10.1016/j.eswa.2010.08.049
[23] Wang X L, Jiang B, Lu N Y. Adaptive relevant vector machine based RUL prediction under uncertain conditions. ISA Transactions, 2018, DOI: 10.1016/j.isatra. 2018.11.024
[24] 24 Yang C H, Yang C, Peng T, Yang X Y, Gui W H. A fault-injection strategy for traction drive control systems. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5719−5727 doi: 10.1109/TIE.2017.2674610
[25] 25 Yang X Y, Yang C H, Peng T, Chen Z W, Liu B, Gui W H. Hardware-in-the-loop fault injection for traction control system. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2018, 6(2): 696−706 doi: 10.1109/JESTPE.2018.2794339