A small-sized fire detection method based on the combination of the SIC algorithm and 1-DCNN

The article proposes a method for detecting small-sized fires using an Ultra-Weak Fiber Bragg Grating (UWFBG) array fire monitoring system, addressing issues of slow response speed and insufficient spatial resolution in traditional fire monitoring systems. The system's spatial resolution is improved by regional subsection monitoring and using the Spectral Intensity Change (SIC) algorithm combined with a one-dimensional convolutional neural network (1-DCNN) model for hot spot recognition. This approach can ignore the impact of noise on the reflected spectra to some extent, overcoming the limitations of traditional methods. Simulation results demonstrate that when the signal-to-noise ratio (SNR) is above 10 dB, the accuracy of hot spot recognition is significantly improved. At an SNR of 15 dB, the recognition accuracy reaches 98.5 %. Furthermore, addressing the spectral distortion issue in the sensor array, introducing the 1-DCNN model achieves a recognition accuracy of 98.6 %. The proposed method can identify small-sized fires of 10 cm, providing a safe and reliable solution for fire monitoring systems in subway tunnels.