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 PointGrey, 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.

In order to overcome the characteristics of uncertainty and incomplete information of fault attribute in searching for fault, a new fault search method based on the fault tree and grey fuzzy multi-attribute decision-making is proposed in this paper. Firstly, utilizing fault tree analysis (FTA) method to determine the initial fault alternative, fault probability and search cost of the fault alternative are considered as fault attributes simultaneously, the attribute value of fault alternative is described as grey fuzzy numbers, calculating the attribute weight by the information entropy method. Then, the grey relational grade between the fault alternative and the ideal alternative is calculated base on grey relational analysis method, and relative closeness of fault alternative is achieved. Finally, according to the relative closeness determine the search order of all fault alternatives. An engineering example of the hydraulic suspension system of a certain type truck is adopted to demonstrate the feasibility and accuracy of the proposed method.