System and method for detecting fire event in underground utility tunnels based on acoustics

A system for early detection of fire situations in an underground facility based on sound, the system comprising:

• a sound acquisition device that is installed in the underground facility and collects acoustic signals in real time;
• and
• a fire situation early detection server that predicts occurrence of electric sparks and infers a fire risk based on the acoustic signal collected by the sound acquisition device,
• wherein the fire situation early detection server includes:
• an audio receiving unit configured to receive the acoustic signals collected from the sound acquisition device;
• a preprocessing unit configured to divide the received acoustic signals into a plurality of frames and obtain a spectrogram for each of the plurality of frames;
• a fire sound analysis unit configured to extract noise-removed data from the obtained spectrogram for each of the plurality of frames;
• a pre-fire situation detection unit configured to predict, for each of the plurality of frames, a probability of electric spark occurrence based on the extracted noise-removed data;
• and
• a fire risk inference unit configured to infer the fire risk by combining predicted probabilities of electric spark occurrence in each frame;
• wherein the underground facility includes at least one of an underground tunnel or facility containing equipment,
• wherein ventilation fan operation generates environmental noise,
• wherein the fire sound analysis unit is configured to generate a noise mask from the obtained spectrogram using a fire sound analysis model, and extract the noise-removed data by subtracting the generated noise mask from the obtained spectrogram,
• wherein the sound acquisition device comprises an audio acquisition unit configured to collect acoustic signals in real time through an acoustic sensor,
• wherein the fire situation early detection server further comprises a decision support unit configured to perform fire situation prediction and generate decision-making support information, and
• wherein the decision support unit is configured to predict a fire situation based on an inferred fire risk level calculated for the acoustic sensor.