A New Fault Diagnosis Method of Flexible OSC Clamp

Abstract

Abstract The capacity and safety of train transportation mostly depends on the working state of the flexible overhead contact system (OCS) clamp in the traction power supply system. The notable feature of the clamp loosening problem is its temperature change. Due to the influence of long-distance measurement, environmental factors, the traditional signal processing methods for temperature measurement have basically failed. The feature analysis method combined with empirical clustering and time series analysis can effectively determine the thermal health status of bare conductors in the overhead contact line and realize the diagnosis of the thermal state. In order to eliminate the much interference in measurement process, the data is pre-processed based on the point density criterion, and then the clustering method of big data is used to calculate the relative proximity of the sampled data, the normal data and the historical fault data. And the time-series similarity analysis method is used to obtain the predicted fault development time of the hidden equipment. Experiments show that the method can accurately identify the thermal health of bare conductors, the concept is clear and the algorithm is simple.