Research on an Intelligent Monitoring Method for Abnormal Temperature Rise in High-Voltage Cables Based on Deep Learning

This study proposes a distributed fire monitoring solution based on weak-reflection FBG (fiber Bragg grating) and deep learning, addressing the need for high-precision monitoring in enclosed spaces such as cable tunnels. The scheme employs multi-channel optical switches, where each channel is divided into positioning (reflectance encoding) and sensing (temperature state identification) sections. An experimental platform was set up to simulate temperature conditions, and 1-DCNN (1-D convolutional neural network) combined with CDAE (convolutional denoising autoencoder) was used for noise reduction processing of spectral data. The results show that the combined model reduces detection errors, and channel testing validates positioning accuracy. The scheme has been experimentally validated as effective, and future improvements can be made through process optimization and integration of technologies to enhance performance.