Abstract
In this paper, rail track irregularity detection system based on computer vision and SVD analysis is proposed and located in the train's operator cabin near the front. Images are captured by FLEA3 camera of Point-Grey, and vibration signals are collected by sensor device MPU6050 integrating 3-axis accelerometer and 3-axis gyroscope. Root mean square of gray-scale threshold Pulse Coupled Neural Network (RMS-PCNN) is used for segmentation of the rail track's image in a single loop, and the improved coupled map lattice(CML) is used for filtering the image and signifying the rail track. After perspective, the track radius can be fetched by analysis of regression. Vibration signal filtered by SVD-unscented Kalman filter(UKF) can reflect the wagon movements. In unscented Kalman filter, Cholesky is replaced by SDV in UT(unscented transform), which can solve negative definite matrix caused by covariance matrix on account of calculation error and round-off error. Also numerical stability is improved under the guarantee of filtering accuracy and the same complexity level of algorithm based on SVD-UKF. Looking up the radius record table, the corresponding threshold in gyroscope signal can be selected, and Compared to the super elevation, the invisible irregularity defects of rail bed will be found out.
Highlights
With the spread of high-speed trains and the increasing costs of road transport, railway research for technical improvements and cost reduction has increased notably during the last decade
The results show that as the rail bed modulus or stiffness increases, the acceleration of track vertical vibration increases, but the vertical vibration velocity of track, the induced ground displacement, velocity, and acceleration decreases[8]
Because the camera is fixed in the front of operate cabin, assumed it is paralleled to rail tracks at the nearest point
Summary
With the spread of high-speed trains and the increasing costs of road transport, railway research for technical improvements and cost reduction has increased notably during the last decade. The practical monitoring of wheel defects for trains could be done through track mounted sensors and the measured data are processed by an advanced calculation program before being combined with the identification tag of a locomotive or a coach. This technique is employed by existing condition-monitoring systems and determines precisely which part of the train is faulty/damaged and to what extent. This invited Special Paper describes in detail the large-scale laboratory tests imperative for material characterization, fullscale instrumented field trials for performance verification, elasto- plastic finite element analyses for predicting the behavior of tracks stabilized using shock mats, and photosynthetic products including grids and prefabricated drains[16]
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