How artificial intelligence applied to sound recognition can help gas leak detection in acoustically complex environments?

Abstract

From production to storage and transport, detecting leaks of flammable gases is a major challenge, particularly in terms of occupational safety and health, and environmental considerations. The emergence of hydrogen as an energy source makes acoustic-based technologies a key element in this value chain. For these reasons, Metravib Engineering, in collaboration with the R&D Safety Project of TotalEnergies One Tech, has been developing since 2019 the AGLED system, for the early detection, localization and classification of these leaks. The system performs acoustic detection in the audible frequency range, using antennas consisting of 4 microphones and physics-based artificial intelligence. Over 5,600 signals were recorded on a site fairly representative of gas production and storage facilities in order to build a robust learning database. Several algorithms were implemented to limit the number of false detections delivered by the system. A relearning strategy was developed to handle unexpected disturbances relative to specific noise environment. This paper describes the overall architecture of the system, from signal acquisition to gas leak detection and 3D localization. It delves into the relearning algorithm and its performance during two full-scale industrial pilots of TotalEnergies - La Mède (France) and Tempa Rossa (Italy) - that included nitrogen controlled leaks tests campaigns.