To address the issues of insufficient sensitivity and low intelligence in traditional communication cable fault detection and localization methods,a fault detection and localization method of communication cables based on machine learning is proposed.Firstly,based on the principle of traveling wave detection,a communication cable fault simulation model is constructed to collect experimental data samples;Then,a communication cable fault detection model based on Particle Swarm Optimization Support Vector Machine(PSO-SVM)is proposed,with a fault recognition accuracy of 99.4%;Next,a communication cable fault location model based on Convolutional Neural Network Long Short Term Memory(CNN-LSTM)is proposed.The average absolute error of the model for fault location is 0.334 9,and the root mean square error is 0.320 8;Finally,through comparative experiments,it was verified that the network accuracy of CNN-LSTM was 9.47%and 6.2%higher than that of using CNN and LSTM models alone,respectively.
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