Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks

Pipelines are one of the most common systems for storing and transporting petroleum products, both liquid and gaseous. Despite the durable structures, leakages can occur for many reasons, causing environmental disasters, energy waste, and, in some cases, human losses. The object of the ESTHISIS project is the development of a low-cost and low-energy wireless sensor system for the immediate detection of leaks in metallic piping systems for the transport of liquid and gaseous petroleum products in a noisy industrial environment. In this study, two distinct leakage detection methodologies are presented. First, a 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. The second methodology entails a Long Short-Term Memory Autoencoder (LSTM AE), which directly receives the signals from the accelerometers, providing an unsupervised leakage detection solution. Field tests for the validation of our methods were performed using an experimental pipeline network, while evaluation of their efficiency in a real environment was conducted in the premises of an oil refinery in Greece. Results evince the potency of the LSTM AE to recognize in real-time the emergence of deficiencies and the efficacy of the CNN models to classify accurately spectrograms reflecting the operational condition of the monitored pipelines.