With the rapid development of urban rail transit, the safety of subway sliding plug doors has become a great concern. To improve the operational reliability of the sliding plug door, we developed a fault detection system based on the adaptive empirical mode decomposition (AEMD). Firstly, we designed a hardware acquisition device and analysis software to collect motor current signal data during the opening and closing of the sliding plug door. Secondly, to address the impact of noise on signal analysis, the AEMD denoising method is proposed. This method employs EMD to obtain intrinsic mode functions (IMFs), and select the appropriate IMF components for reconstruction based on the adaptive threshold of Hausdorff distance, resulting in improved denoising effectiveness. Thirdly, waveform segments of different faults are sliced to reduce the amount of computation and effectively improve recognition accuracy. Meanwhile, this paper utilizes feature selection methods and machine learning techniques to classify the 12 subway sliding plug door faults. It is worth noting that most of these faults have not been extensively studied in previous classification research. The experimental results show that the identification accuracy reaches 98.96% on the practical platform. Moreover, the effectiveness and robustness of our proposed method are further validated through practical tests, ablation experiments, and comparisons with other relevant literature.

A comparative study is presented for the latest bio-inspired optimization algorithms, known as bacterial foraging, firefly, cuckoo search, krill herd, and cuttlefish algorithms. This study also included the more established genetic and particle swarm optimization methods. These algorithms are then applied to a partial-fraction model fitting of tabulated measured dielectric function data for noble metals at optical wavelengths.

Using traditional FMECA to analyze the hazard of subway sliding plug door system, there are problems such as easy-to-take repetitive values, irrational allocation of expert's weights, and failure to consider the weights of evaluation factors. Hence, this paper proposes to improve FMECA by using linear interpolation to increase the differentiation of the same fault probability occurrence among various fault modes. Apply the DUOWA to dynamically assign weights to different experts. The AHP is used to endow weights to diverse evaluation factors to make them more suitable for engineering needs. The operation records of metro trains were collected from the Shanghai subway manufacturing plant for 1836 days. The hazards of the 17 common faults of the door were first analyzed using an improved FMECA,the EDCU is the component most prone to failure. Next, use a RCM strategy to determine maintenance periods for different faults. Finally, through the Weibull distribution fitting test, the fault rate function of the door is obtained, and the RUL of the door is predicted.

A SiC double-trench MOSFET embedded with a lower-barrier diode and an L-shaped gate-source in the gate trench, showing improved reverse conduction and an improved switching performance, was proposed and studied with 2-D simulations. Compared with a double-trench MOSFET (DT-MOS) and a DT-MOS with a channel-MOS diode (DTC-MOS), the proposed MOS showed a lower voltage drop (V_F) at I_S = 100 A/cm², which can prevent bipolar degradation at the same blocking voltage (BV) and decrease the maximum oxide electric field (E_mox). Additionally, the gate–drain capacitance (C_gd) and gate–drain charge (Q_gd) of the proposed MOSFET decreased significantly because the source extended to the bottom of the gate, and the overlap between the gate electrode and drain electrode decreased. Although the proposed MOS had a greater R_on,sp than the DT-MOS and DTC-MOS, it had a lower switching loss and greater advantages for high-frequency applications.