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

Area of Science:

• Railway engineering
• Mechanical systems monitoring
• Signal processing

Background:

• Train door actuation systems are critical for safety and operational continuity.
• Conventional monitoring systems often increase complexity and cost.
• Early fault detection is crucial for preventing service disruptions.

Purpose of the Study:

• To propose a novel wavelet-based health monitoring structure for train door actuation systems.
• To detect both electrical and mechanical faults using motor current signal analysis.
• To provide a cost-effective alternative to traditional sensor-based monitoring.

Main Methods:

• Developed a dynamic MATLAB/Simulink model of the train door actuation mechanism (DC motor, gearbox, lead screw).
• Simulated various faults including electrical issues, brush wear, friction, and misalignment.
• Analyzed motor current signals using Discrete Wavelet Transform (Daubechies db10 wavelet).
• Extracted diagnostic features using L1-norms of wavelet coefficients and motor starting current peak.

Main Results:

• Experimental validation achieved fault detection accuracy exceeding 96%.
• The system demonstrated a rapid response time of under 0.3 seconds.
• The wavelet-based approach effectively identified simulated electrical and mechanical faults.

Conclusions:

• The proposed wavelet-based health monitoring system is effective for detecting faults in train door actuation.
• This approach offers a reliable and efficient solution for predictive maintenance in railway systems.
• The method enhances safety and reduces service disruptions by enabling early fault detection.