Compressed air leakage detection using acoustic emissions with neural networks

Compressed air is utilized in many branches of industry and represents one of the most expensive energy sources of industrial plants. The efficient detection of air pressure leaks goes hand-in-hand with cost savings and increased operational reliability. Some procedures of leakage detection for pressure lines are based upon the analysis of sound emissions. Such solutions use specific ultrasonic emission patterns to detect leakage; alternatively, personnel trained to hear leaks are deployed for detection. In this paper, we evaluate the potential of airborne sound analysis in the audible hearing range for the automated detection of compressed air leakage using artificial neural networks. Therefore, a novel dataset is developed and published. It contains recordings of adjustable leakage from a pneumatic contraption with different pressure levels from several microphones at different distances. Additionally, industrial background noises were applied to simulate real-world s ound environments. Using this dataset, a convolutional neural network is trained for leakage detection. The results show that leakage detection by means of airborne sound in the audible range using machine learning techniques is possible and a promising contactless automated detection method.