Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 273-295
Abstract
Refrigerant leaks from cooling systems can harm the environment and cost businesses money. Current ways to find leaks can be slow, expensive, and not always accurate. This project uses machine learning to create a better way to detect refrigerant leaks by listening to the sounds they make. The goal is to develop a system that can automatically and cheaply detect leaks early on, reducing environmental damage and saving businesses money. The system uses a microphone to record sounds, then a computer program analyzes the sounds to identify leaks. By using sound analysis, the system can tell the difference between normal sounds and the sounds of a refrigerant leak. This helps catch leaks early, lowers maintenance costs, and reduces greenhouse gas emissions.