Wavelet-Based Health Monitoring Approach for Train Door Actuation Using Motor Current Analysis

Train door actuation systems are critical safety components in railway vehicles, where early fault detection is essential for safe operation and reduced service disruptions. Conventional monitoring approaches often rely on additional sensors such as infrared detectors or vision systems, which increase system complexity and cost. To overcome these limitations, this study proposes a wavelet-based health monitoring structure for detecting electrical and mechanical faults using motor current signal analysis. A dynamic model of the train door actuation mechanism, including a DC motor, gearbox, and lead screw, was developed in MATLAB/Simulink to simulate conditions such as armature electrical faults, brush wear, increased friction, and lead screw misalignment. Motor current signals were analyzed using the Discrete Wavelet Transform with a Daubechies (db10) mother wavelet to extract diagnostic features based on the L1-norms of wavelet coefficients at levels W8 and W9 along with the motor starting current peak. Experimental validation using a LabVIEW-based test platform demonstrated fault detection accuracy above 96% with a response time below 0.3 s, confirming the effectiveness of the proposed approach for predictive maintenance of railway door systems.