Machine learning methods in the development of leak detection techniques

The aim of the work is to explore the possibility of using artificial intelligence to find leak detection using training data obtained using vibroacoustic sensors. The paper describes methods of continuous monitoring. Methods of periodic monitoring of leak detection in the pipeline are presented. Testing work was carried out on the laboratory stand. Methods for machine learning are considered, namely boosting, k-nearest neighbors, random forest method, multi-layer neural network method. An algorithm has been developed for compiling data arrays. The results of the program are shown.