Identification of threats to pipeline based on optimized support vector machine

Effective identification of threats to the oil/gas pipeline due to artificial excavation, mechanical excavation and vehicles passing is of vital importance to the pipeline optical fiber security precaution system based on coherent Rayleigh. This paper presents a pipeline threat identification method based on optimized support vector machine. The method is used to analyze the vibration signals along the pipeline collected by pipeline optical fiber safety precaution system, extract the normalization energy and signal duration of each frequency band as feature vector, use artificial bee colony algorithm to optimize the penalty factor and kernel function parameter of support vector machine and use the optimized support vector machine to classify features. The safety test was carried out on the GangZao Products Pipeline with the accuracy of classification up to 90.7%, which proves the effectiveness and engineering value of the proposed method.