Online Detection of Railway Track Irregularities via JADE-Based Blind Source Separation and MEMS Accelerometry

Abstract

To address the difficulties in accurately capturing the characteristic changes of track irregularities in real - time and the limited ability to process complex mixed vibration signals, this study proposes an online Detection of Railway Track Irregularities via JADE-Based Blind Source Separation and MEMS Accelerometry. The system consists of a lower computer and an upper computer with ADXL345 three-axis acceleration sensor as the core. Real time track vibration signals are collected through optimized IIC bus protocol, and the blind source separation algorithm based on JADE is executed by STM32F103ZET6 microprocessor. By jointly diagonalizing the mixed vibration signal through a fourth-order cumulative matrix, the track roughness feature components in the mixed vibration signal are effectively decoupled, achieving accurate detection of railway track roughness. The detection results are converted into USB signals through RS-232 serial port and CH340G chip, and uploaded to the upper computer. The upper computer platform visualizes the type, location, and severity of track roughness faults. At the same time, a dual level power management and anti reverse protection are designed to ensure the reliability of the railway environment. To verify system performance, 8 monitoring points were set up on a 30 kilometer actual operating line, and multiple sets of vibration data were continuously collected at a sampling frequency of 10240 Hz at train speeds of 60-80 km/h. Establish the ground truth value of faults through high-precision track inspection vehicles and total station measurements, and compare it with HybridGAN method and data mining method. The experimental results show that this system can achieve an average positioning error of ≤ 1.8 mm, a fault type recognition accuracy of ≥ 96%, and an average detection time of ≤ 90 ms at a speed of 60 km/h. At a speed of 80 km/h, it still maintains an error of ≤ 2.2 mm and a recognition accuracy of ≥ 90%, with better performance than the two comparison methods. The upper computer of the system has the function of visualizing fault types, locations, and degrees, and integrates dual level power management and anti reverse protection, which is suitable for complex railway environments. This system provides a feasible solution for real-time monitoring of track status with high accuracy and low latency.