Citation: Spandonidis, C.;
Theodoropoulos, P.; Giannopoulos, F.
A Combined Semi-Supervised Deep
Learning Method for Oil Leak
Detection in Pipelines Using IIoT at
the Edge. Sensors 2022, 22, 4105.
https://doi.org/10.3390/s22114105
Academic Editors: Hamed Badihi,
Tao Chen and Ningyun Lu
Received: 5 May 2022
Accepted: 26 May 2022
Published: 28 May 2022
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Article
A Combined Semi-Supervised Deep Learning Method for Oil
Leak Detection in Pipelines Using IIoT at the Edge
Christos Spandonidis * , Panayiotis Theodoropoulos and Fotis Giannopoulos
Prisma Electronics SA, Leof. Poseidonos 42, 17675 Kallithea, Greece;
panagiotis.theodoropoulos@prismael.com (P.T.); fotis.giannopoulos@prismael.com (F.G.)
* Correspondence: c.spandonidis@prismael.com; Tel.: +30-694-852-2088
Abstract:
Pipelines are integral components for storing and transporting liquid and gaseous petroleum
products. Despite being durable structures, ruptures can still occur, resulting not only in financial
losses and energy waste but, most importantly, in immeasurable environmental disasters and pos-
sibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost
and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline
networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The
implemented methodology is based on processing the spectrum of vibration signals appearing in the
pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is
intended to use low frequencies to detect and characterize leakage to increase the range of sensors
and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition
and data analysis is briefly described. For this matter, two leakage detection methodologies are imple-
mented. 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. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed,
receiving signals from the accelerometers, and providing an unsupervised leakage detection solution.
Keywords:
leakage detection; oil pipeline; deep learning; CNN classifier; LSTM autoencoders;
edge computing
1. Introduction
During recent decades, pipeline networks have been considered among the safest and
most economical methods for transporting and storing oil and gas products [
1
]. In fact,
pipeline infrastructure is critical for worldwide economic growth. Multiple investments in
hydrocarbons and petrochemical facilities are materialized thanks to the steady and reliable
supply of feedstocks provided by pipeline infrastructure [
2
]. For example, it has been esti-
mated that, in 2015, crude oil pipelines generated approximately 200,000 jobs, accumulating
over $21.8 billion in Gross Domestic Product [
3
]. Consequently, oil piping installations
worldwide have been rapidly expanding to satisfy the ever-increasing energy needs of the
population, intricating the topological complexity of the pipeline network, perplexing its
supervision and assessment of its safety [
4
]. Additionally, this breadth of pipeline usage in-
herently aggrandizes the probability of structural defects due to erosion over time, fracture
propagation, human factors, environmental factors, and other
causes [5–7]
. Leak detection
in pipelines has been a prevalent issue for several decades. Pipeline leaks from sources
such as small cracks and pinholes are termed chronic leaks, as they have the potential of
going unnoticed for a long period of time, causing irreversible damage [
8
]. Even seemingly
small defects scale up fast to unfathomable magnitude. For instance, on 2 March 2006, a
spill of about 1 million liters of oil occurred over around five days in the area known as
Alaska’s North Slope because a quarter-inch hole corroded in a pipeline [
9
]. Therefore,
Sensors 2022, 22, 4105. https://doi.org/10.3390/s22114105 https://www.mdpi.com/journal/sensors
