Abstract: To address the challenge of predicting cable fires, this paper proposes a prediction technology for cable ignition caused by fault arcs. In the research, an experimental platform was established to simulate the environment of arc fault ignition. Five common types of cable sheath materials were selected, and through experiments, the ignition conditions of cables under arc fault conditions were simulated. To build a more precise prediction model, this paper introduces multiple scientific evaluation indicators, such as precision and accuracy. The synthetic minority over-sampling technique (SMOTE) was used to optimize the data. Based on this, an artificial neural network (ANN) model was established with a 5-fold cross-validation step to verify the stability and reliability of the model, thereby achieving accurate prediction of cable ignition conditions. In addition, a comparative analysis was conducted with the Support Vector Machine (SVM) model. The final results show that the accuracy of the ANN model is as high as 85.7%, significantly better than the 72.73% accuracy of the SVM model. The results indicate that the proposed model can effectively predict cable ignition, providing a scientific basis for electrical fire prevention and the safety design of cable systems.

Key words: cable fire, synthetic minority over-sampling technique, fault arc, artificial neural network