Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks
Title
Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks
Subject
CNN classifier
Deep learning
Leakage detection
LSTM autoencoders
Oil pipeline
Description
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.
104890
113
Creator
Spandonidis, Christos
Theodoropoulos, Panayiotis
Giannopoulos, Fotis
Galiatsatos, Nektarios
Petsa, Areti
Publisher
Engineering Applications of Artificial Intelligence
Date
2022
Type
journalArticle
Identifier
0952-1976
10.1016/j.engappai.2022.104890
Collection
Citation
Spandonidis, Christos et al., “Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks,” Lamar University Midstream Center Research, accessed May 14, 2024, https://lumc.omeka.net/items/show/26928.