Applying One Class Classification for Leak Detection in Noisy Industrial Pipelines

In this work, a machine learning approach is proposed for the problem of leak detection in noisy industrial pipelines. The traditional multi-class or binary classification approaches depend on the fact that real training data are required. However, in real pipeline scenarios the data generation for the leak class relies on measurements of artificially generated leaks which are different in nature than actual ones. Also, some pipelines are not equipped with the components to generate these leaks and in some cases, it is difficult to acquire a large and balanced leak dataset. Thus, in this paper, a set of one class classification models are applied that do not require training with real leak data. In this study's case, four one class classification models are trained on a single class representing the pipeline's normal operating noise. Seven time and frequency domain features are extracted from the raw acoustic data acquired by a set of accelerometers. The trained models are then tested on a new dataset containing leak and noise measurements. This dataset is used to evaluate each model's ability to detect leaks as well as the effect that some introduced parameters have on their performance. Overall, high levels of accuracy are exhibited, and all models are able to distinguish between noise and leak data.