An Adaptive Fault Diagnosis Model for Railway Single and Double Action Turnout

As a key equipment to switch the direction of a running train, railway turnout works in complex condition which makes its fault diagnosis difficult. Generally, existing methods identify the fault by analyzing the turnout action curve acquired by sensors, which have certain practical value for fault diagnosis, but poor practicability for varied types like double or multiple action turnout. In this paper, fault detection is carried out according to the distance between the normal current curve and the test curve calculated by fast dynamic time warping algorithm. In view of the singular point problem involved, a segmentation method for current curve based on the key nodes in the turnout conversion process is proposed and applied to the fault detection of single action and double action turnouts. Experimental results show that proposed approach can effectively improve the matching accuracy of adaptive diagnosis model which is more than 96%. Furthermore, compared with the traditional dynamic time warping algorithm, the time cost can be reduced by more than 5 times.