REFERENCES
1. Yong Q, Hui M, Li-Min J. Development trend and active safety technology for advanced rail transit system. China Railway 2015;2015:77-81.
2. Cheng C, Sun X, Song Y, et al. A just-in-time manifold-based fault detection method for electrical drive systems of high-speed trains. Simu Model Prac Theor 2023;127:102778.
3. Wei W, Lin Y. Simulation of a freight train brake system with 120 valves. Proc Inst Mech Eng Part F J Rail Rapid Transit 2009;223:85-92.
4. Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009;1:1-41.
5. Su S, She J, Wang D, Gong S, Zhou Y. A stabilized virtual coupling scheme for a train set with heterogeneous braking dynamics capability. Transp Res Part C: Emerg Technol 2023;146:103947.
6. Dong H, Ning B, Cai B, Hou Z. Automatic train control system development and simulation for high-speed railways. IEEE Circuits Syst Mag 2010;10:6-18.
7. Wang T, Wang W, Zio E, Tang T, Zhou D. Analysis of configuration data errors in Communication-based Train Control systems. Simul Model Pract Theory 2019;96:101941.
8. Yang C, Yang C, Peng T, Yang X, Gui W. A fault-injection strategy for traction drive control systems. IEEE Trans Ind Electron 2017;64:5719-27.
9. Ding M, Chen B. Secure consensus control for multi-agent systems under communication constraints via adaptive sliding mode technique. Complex Eng Syst 2023;3:7.
10. Abboush M, Knieke C, Rausch A. Representative real-time dataset generation based on automated fault injection and HIL simulation for ML-assisted validation of automotive software systems. Electronics 2024;13:437.
11. Joshi A, Miller SP, Whalen M, Heimdahl MP. A proposal for model-based safety analysis. In: 24th Digital Avionics Systems Conference. vol. 2. IEEE; 2005. pp. 13-pp.
12. Linnosmaa J, Pakonen A, Papakonstantinou N, Karpati P. Applicability of AADL in modelling the overall I&C architecture of a nuclear power plant. In: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 2020, pp. 4337-4344.
13. Liu T, Yu J, Sun WJ. Study on fault-tolerant technique based on knowledge modules of hydraulic fault theory. In: Advanced Materials Research. vol. 712. Trans Tech Publ; 2013. pp. 2043-50.
14. Karpenko M, Sepehri N. Fault-tolerant control of a servohydraulic positioning system with crossport leakage. IEEE Trans Control Syst Technol 2004;13:155-61.
15. Niksefat N, Sepehri N. Fault tolerant control of electrohydraulic servo positioning systems. In: Proceedings of the 2001 American Control Conference. (Cat. No. 01CH37148). vol. 6. IEEE; 2001. pp. 4472-77.
16. Kiamanesh Bahareh BAOR. Fault Injection with Multiple Fault Patterns for Experimental Evaluation of Demand-Controlled Ventilation and Heating Systems. Sensors 2022;22:8180.
17. Zhou D, Ji H, He X, Shang J. Fault Detection and Isolation of the Brake Cylinder System for Electric Multiple Units. IEEE Trans Contr Syst Technol 2018;26:1744-57.
18. Gou B, Ge X, Wang S, et al. An open-switch fault diagnosis method for single-phase PWM rectifier using a model-based approach in high-speed railway electrical traction drive system. IEEE Trans Power Electron 2016;31:3816-26.
19. Youssef AB, El Khil SK, Slama-Belkhodja I. State Observer-Based Sensor Fault Detection and Isolation, and Fault Tolerant Control of a Single-Phase PWM Rectifier for Electric Railway Traction. IEEE Trans Power Electron 2013;28:5842-53.
20. Chen Z, Peng L, Fan J, et al. EPBSFIDMV: A fault injection and diagnosis methods validation benchmark for EPBS of EMU. Contr Eng Pract 2024;145:105873.
21. Qin SJ. Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control 2012;36:220-34.
22. Huang M, Yin J, Yan S, Xue P. A fault diagnosis method of bearings based on deep transfer learning. Simul Model Pract Theory 2023;122:102659.
23. He J, Lv Z, Chen X. Rolling bearing fault diagnosis method based on 2D grayscale images and Wasserstein Generative Adversarial Nets under unbalanced sample condition. Complex Eng Syst 2023;3:13.
24. Fang D, Peng T, Yang C, Chen Z, Tao H. Random-Sampling-Based Performance Evaluation Method of Fault Detection and Diagnosis for Railway Traction System. In: 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). IEEE; 2019. pp. 570-74.
25. Chen Z, Fan J, Peng L, et al. Multiple Markov chains-based layered random fault injection method for the air braking system. In: 2023 IEEE 2nd Industrial Electronics Society Annual On-Line Conference (ONCON), SC, USA, 2023, pp. 1-6.
27. Mcginnis R, Isaacson DL, Madsen RW. Markov Chains Theory and Applications. Contemp Sociol 1977;6:322.
28. Sericola GRB. Markov Chains and Dependability Theory Cambridge: Cambridge University Press; 2014.
29. Grassmann WK, Taksar MI, Heyman DP. Regenerative analysis and steady state distributions for Markov chains. Oper Res 1985;33:1107-16.
30. Azimi S, Hassannayebi E, Boroun M, Tahmoures M. Probabilistic analysis of long-term climate drought using steady-state Markov chain approach. Water Resour Manage 2020;34:4703-24.
31. Bod LWM. Introduction to probability theory and formal stochastic language theory. Probabilistic Linguistic 2003; doi: 10.7551/mitpress/5582.003.0005.
32. Smith JC, Jacobson SH. An analysis of the alias method for discrete random-variate generation. INFORMS J Comput 2005;17:321-7.
