Machine Learning Model Comparison for Leak Detection in Noisy Industrial Pipelines

Abstract

In this paper, two machine learning techniques are applied and compared in order to model leak detection in pipelines in noisy environments. A set of accelerometers, mounted on the surface of the pipes, was deployed for the data acquisition process. Measurements of noise during normal operating conditions were recorded as well as measurements of leaks, generated on various distances from the sensors. Using these measurement data, a training set was created from their time-domain and frequency-domain features. The leak detection process is then modeled as a binary classification problem (leak detection or not). For this problem, two machine learning classification techniques were evaluated, the support vector machines and the decision trees. The results for each learner are compared with the original data from the test dataset using representative performance indicators and, overall, high levels of accuracy are achieved.