Virtual coupling has been recognized as a promising yet challenging technology for the next generation of train control systems. As a mean of guaranteeing the safety of closer-running of virtually-coupled electric multiple units (EMU) trains, fault detection and diagnosis play a critical role in perceiving abnormal running conditions. This paper develops a new signed-data reinforced observer-based technique for dealing with actuator faults’ detection and diagnosis for virtually-coupled EMU trains. The introduction of novel signed-data reinforcement technique ensures that unbiased fault diagnosis can be guaranteed with any regressor vector composed of control signals on EMU trains. Fault detection and diagnosis observers, together with fault alarming principle, information transmission coding strategy are elaborated using Lyapunov stability theorem. Simulation results are given to demonstrate the effectiveness of proposed algorithm.

中文翻译：

---

符号数据增强的虚拟耦合电动动车组基于观测器的故障诊断

虚拟耦合已被认为是下一代列车控制系统的一项有前途但具有挑战性的技术。作为保证虚拟耦合动车组列车紧密运行安全的手段，故障检测与诊断在感知异常运行状况方面发挥着至关重要的作用。本文开发了一种新的基于符号数据增强的观测器的技术，用于处理虚拟耦合动车组列车的执行器故障检测和诊断。新颖的有符号数据强化技术的引入，确保动车组列车上由控制信号组成的任何回归向量都能保证无偏的故障诊断。利用Lyapunov稳定性定理详细阐述了故障检测与诊断观测器、故障报警原理、信息传输编码策略。仿真结果证明了所提算法的有效性。