A pipeline burst or rupture causing a leak may significantly impact the environment and the reputation of the company operating the pipeline. In recent years, oil and gas pipelines are expected to be equipped with leak detection systems for monitoring the operations and detecting the leaks. Although the leak detection methods used today may not prevent leaks from happening, they play a crucial role in limiting the impact of leak. There is a wide variety of leak detection methods developed and tested. This paper reviews these methods, analyze their advantages and limitations. It ends by highlighting the opportunities for future work to improve reliability and adaptability of leak detection methods in subsea region. 

Review and analysis of pipeline leak detection methods

A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

Increasing urbanisation and a better understanding of the negative health effects of air pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low-cost and low-power sensors are now readily available and commonly deployed by individuals and community groups. However, there are a wide range of such IoT devices in circulation that differently focus on problems of sensor validation, data reliability, or accessibility. In this paper, we present AirKit, which was developed as an integrated and open source “social IoT technology”. AirKit enables a comprehensive approach to citizen-sensing air quality through several integrated components: (1) the Dustbox 2.0, a particulate matter sensor; (2) Airsift, a data analysis platform; (3) a reliable and automatic remote firmware update system; (4) a “Data Stories” method and tool for communicating citizen data; and (5) an AirKit logbook that provides a guide for designing and running air quality projects, along with instructions for building and using AirKit components. Developed as a social technology toolkit to foster open processes of research co-creation and environmental action, Airkit has the potential to generate expanded engagements with IoT and air quality by improving the accuracy, legibility and use of sensors, data analysis and data communication.

https://www.mdpi.com/1424-8220/21/12/4044/pdf

AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality.

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 possibly 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 implemented. 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.

/pdf/a-combined-semi-supervised-deep-learning-method-for-oil-leak-1l16cdv4.pdf

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge

/pdf/influencing-factors-of-smart-community-service-quality-1xl5kvn36v.pdf

Influencing Factors of Smart Community Service Quality: Evidence from China

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

/pdf/a-comparative-study-of-wireless-communication-protocols-in-a-1mdct4ji9b.pdf

A Comparative Study of Wireless Communication Protocols in a Computer Vision System for improving the Autonomy of the Visually Impaired

A compact, modular and low-cost Internet of Things (IoT) platform for air quality monitoring in urban areas

Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations.

/pdf/a-novel-intelligent-iot-system-for-improving-the-safety-and-1n0v7ccu.pdf

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

Pipelines are the primary means for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur causing not only financial losses and energy waste but also incalculable environmental disasters and possibly human casualties. The main purpose of the ESTHISIS project is the development of a low-cost wireless sensor system for instant leakage detection and localization, and early warning platform for Oil and Gas pipeline networks. These networks transport liquid and gaseous petroleum products in noisy industrial environments. The methodology used is based on statistical analysis and feature extraction for the leak detection procedure and spectral and temporal segmentation of the leak signals for the localization process. The data acquisition systems are intended to operate at low frequencies to detect and characterize the leakages occured. The integration of highaccuracy data acquisition, precise synchronization between devices, advanced algorithms application and real time data visualization in an early warning Web Application creates a competitive leakage detection solution. 

Nektarios Galiatsatos

Papers

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

A Comparative Study of Wireless Communication Protocols in a Computer Vision System for improving the Autonomy of the Visually Impaired

A compact, modular and low-cost Internet of Things (IoT) platform for air quality monitoring in urban areas

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

Industrial internet of things platform for leak detection and localization in Oil and Gas pipelines