Abstract:

Against the problem of rapidly and accurately classifying different fire sources and interference sources by aircraft fire detection system, a recurrent neural network fire detection algorithm based on long short- term memory was proposed,which can consider the vital time dynamic information in the fire signal. Through the experiment of different fire and interference sources, and based on the signals of dual wavelength, CO and temperature measured by the composite fire detector, the network classification model was trained by connecting them into a characteristic sequence according to time. Through the different selection methods of length of time series data sets, the combustion intensity was simulated, and the robustness, rapidity and accuracy of the algorithm were validated. The results showed that the network is effective in the problem of fast classification of true ignition source and interference source.

Key words:

fire detection, recurrent neural network, long short - term memory, fast classification