Pipeline Leak Detection System Research Articles

Pipeline Leak Detection System for a Smart City: Leveraging Acoustic Emission Sensing and Sequential Deep Learning

This study explores a novel approach utilizing acoustic emission (AE) signaling technology for pipeline leakage detection and analysis. Pipeline leaks are a significant concern in the liquids and gases industries, prompting the development of innovative detection methods. Unlike conventional methods, which often require contact and visual inspection with the pipeline surface, the proposed time-series-based deep learning approach offers real-time detection with higher safety and efficiency. In this study, we propose an automatic detection system of pipeline leakage for efficient transportation of liquid (water) and gas across the city, considering the smart city approach. We propose an AE-based framework combined with time-series deep learning algorithms to detect pipeline leaks through time-series features. The time-series AE signal detection module is designed to capture subtle changes in the AE signal state caused by leaks. Sequential deep learning models, including long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and gated recurrent units (GRUs), are used to classify the AE response into normal and leakage detection from minor seepage, moderate leakage, and major ruptures in the pipeline. Three AE sensors are installed at different configurations on a pipeline, and data are acquired at 1 MHz sample/sec, which is decimated to 4K sample/second for efficiently utilizing the memory constraints of a remote system. The performance of these models is evaluated using metrics, namely accuracy, precision, recall, F1 score, and convergence, demonstrating classification accuracies of up to 99.78%. An accuracy comparison shows that BiLSTM performed better mostly with all hyperparameter settings. This research contributes to the advancement of pipeline leakage detection technology, offering improved accuracy and reliability in identifying and addressing pipeline integrity issues.

Optimization of Pipeline Leakage Detection System in Utility Tunnel Based on Finite Element Method

In addressing the problems of delayed detection, inefficient identification, and coverage blind spots in optical fiber‐based pipeline leakage detection within pipe galleries, this study proposes a leakage detection strategy utilizing distributed optical fiber temperature measurement technology. Finite element method is employed to analyze the temperature influence radius around the pipeline leakage hole and a model test is conducted as a validation. The results show that the temperature field image at the leakage site is elliptical, influenced by temperature differences between ambient and liquid. An increase in this temperature difference accelerates changes in the temperature field’s range. Adjustments to the optical fiber’s winding angle and pitch demonstrated that an optimal pitch is 1/24 of the pipeline’s length, with a 45° winding angle. This configuration maximizes the optical fiber’s distribution in detection while maintaining its cost‐effectiveness. When the leakage site is constant, and only the winding mode is altered, it is observed that when the ambient temperature exceeds the liquid temperature in the pipeline, the temperature of the escaping liquid impacts the temperature‐measuring fiber due to gravity, registering approximately 2°C higher than the temperature measured directly at the leakage site. The temperature anomaly from field diffusion is significantly less than that caused by the water flow from the leakage impacting the fiber due to gravity. Conversely, when the ambient temperature is lower than the pipeline’s liquid temperature, the opposite occurs. These research findings offer a novel approach for distributed detection in water supply and drainage pipeline leakage.

Pipeline Leak Detection System

This article deals with a development of a pipeline leak detection system for monitoring hydrocarbon pipelines in real time. This system is based on the integration of methods of negative pressure waves of propagation and mass and momentum balance calculation. The system is designed to receive real-time data from multiple remote terminals which operate in the field and its processing in a monitoring center. For validation of the system, a liquefied gas pipeline of 20” diameter and 60 km in length was used. The validation of the system was done by comparing the events detected and located by the developed system and the results of verification of real leak locations. Some inspection results using this system are presented.

LoRaWAN-based Petroleum Pipeline Leakage Detection System Using Pressure Profile under a Pump Proximity Effect Condition

Leakages in pipeline is an important problem that can occur at any stage of the pipeline lifespan due to ageing, improper installation, or human related factors like bunkering or vandalization. Several invasive and non-invasive techniques are being used which have proven to be successful. However, this study focuses on leakage in an area of the pipeline network that is most often overlooked. This area is within the first 100 meters of the pipeline network to the excitation pump and is often prone to leakages due to high pressure. This study shows that even at no leak conditions pressure profile of a short pipeline in a closed loop configuration can vary by as much 59.93 percent. The study also examined the impact of single leak, double leaks, and triple leaks on the pipeline network with pressure loss compared to the no leak condition ranging from about 10 percent to as high as 60 percent.

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.

Developing and Implementing an AI-Based Leak Detection System in a Long-Distance Gas Pipeline

This research proposes an artificial intelligence (AI) detection model using convolutional neural networks (CNN) to automatically detect gas leaks in a long-distance pipeline. The change of gap pressure is collected when leakage occurs in the pipeline, and thereby the feature of gas leakage is extracted for building the CNN model. The gas leak patterns in the long-distance pipeline are analyzed. A pipeline detection model based on AI technology for automatically monitoring the leaks is proposed by extracting the feature of gas leakage. This model is tested by collecting gas pressure data from an existing natural gas pipeline system starting from Mailiao to Taoyuan in Taiwan. The testing result shows that the reduced model of leak detection can be used to detect the leaks from the upstream and downstream pipelines, and the AI-based pipeline leak detection system can obtain a satisfactory result.

An infrasound source localisation algorithm for improving location accuracy of gas pipeline leakage detection system

An infrasound source localisation algorithm for improving location accuracy of gas pipeline leakage detection system

An infrasound source localisation algorithm for improving location accuracy of gas pipeline leakage detection system

An infrasound source localisation algorithm for improving location accuracy of gas pipeline leakage detection system

Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance.

RETRACTED: Design and research on wireless intelligent monitoring system for sewage pipeline leakage of textile mill

The traditional textile factory sewage pipeline leakage detection system has many defects, such as high packet loss rate, low accuracy and low system alarm response rate. This paper designs a new wireless intelligent monitoring system for sewage pipeline leakage of textile mill. Firstly, the DSP chip, analog-to-digital conversion circuit, MCU, LCD interface module and communication module were designed. After the design of each module, the efficient processing and spectrum analysis of water leakage acoustic digital signal were achieved, the synchronous acquisition of detection instrument data was realized, thus improving the operation frequency of the system. Secondly, the pipeline leakage location function was designed. Through the power spectrum, the leakage sound wave spectrum was analyzed to obtain the important characteristics of the leakage sound wave, and then the leakage detection was implemented. Thus, achieving the textile factory sewage pipeline leakage wireless intelligent monitoring. The results show that the system designed in this work reduces the monitoring packet loss rate, the alarm response time and false alarm rate, and improves the monitoring accuracy.

Crude Oil Pipeline Security: Minimizing Illegal Tapping using “SOLIDS” System

One of subsidiaries of Pertamina is Pertamina Gas which manage special task in operating crude oil transportation 15,000 barrel oil per day (BOPD). In the operation still occur illegal tapping activities and risk of pipeline product theft is a major concern to industry. In 2012, oil thieves drills 748 illegal taps or an average 2 times every day. Losses from transportation approximately 40% per day and loss revenue more than $20 million a year. The activities of illegal tapping by cutting into pipelines can cause pipeline ruptures and explosions, leading to human casualties, destruction of property, and damage to the environment. Pertamina Gas reported that illegal taps rise to 400% from 2010 until 2013 and effort was taken to minimize illegal tapping frequency and develops integrated system that includes supervision and security of assets along the pipeline called “Security and Oil Losses Management with Integrated Detection System (SOLIDS)”. This system includes liquid management system (LMS), pipeline leak detection system (PLDS), security team patrol, emergency response team (ERT), radio communication-CCTV and corporate social responsibility (CSR). The implementation of SOLIDS is an effective oil loss detection technologies and pipeline security that detect product thefts quickly and accurately locate illegal tapping points. SOLIDS investment costs are still smaller than the company's losses due oil losses and environmental impact.Pertamina Gas has been succeeded in reducing losses from illegal taps. In 2012 the number of illegal tapping cases 748 points and decreased significantly in 2018 as many as zero case. Consistent implementation of this system will provide solution in reducing losses and illegal tapping under all operational conditions.

A Review on Water Leakage Detection Method in the Water Distribution Network

Leak detection in transmission pipelines is crucially essential for safe operation. Pipeline leak detection systems play a crucial role to minimize the probability of occurrence of leaks and hence their impacts. The distribution pipelines in case of urban water supply need to monitor for contaminants such as microbial growth, internal corrosion of the pipe’s material and other deposits. In addition to the loss of water resources, the contaminant can be infiltrated into the piping system. These contaminants affect not only the quality of the water but also the smoothness of the water pipe flow due to the pressure loss and additional frictions. Therefore, it is essential that this problem be quickly detected and repaired. Today there are many available technologies in the domain of leak detection. This paper will provide you with a fundamental understanding of the operating principles of currently available pipeline leak detection technologies.

Iot Enabled Pipeline Leakage Detection and Real Time Alert System in Oil and Gas Industry

Automation and control systems are necessary throughout oil & gas industries, to production and processing plants, and distribution and retailing of petroleum products. Pipelines are the efficient mode of transportations of fuels for processing plants over long distances. At present Automation is achieved by using PLC’s that are communicated through SCADA. But it is complex and remote operation is not possible. With the introduction of IoT, the pipeline leak detection system is improved through real-time monitoring of the pipelines. Our Proposed system is designed to detect even small leakage that occurs within the pipeline. The implementation of IoT in oil and gas industries prevents accidents and to make quick decisions based on real-time data.

Performance evaluation of ADMC-MAC mission critical protocol for wireless sensor networks

Mission-critical MAC protocol is an attractive research area since past few years as the wireless sensor networks have developed their utility in mission-critical applications like border surveillance, Tsunami alert systems, and earthquake safety systems, fire fighting applications, industrial automation, pipeline leakage detection system, critical health monitoring, etc. The Wireless sensor networks are deployed for this purpose may be termed as Mission-critical wireless sensor networks, and MAC protocols designed are termed as Mission Critical MAC Protocols for WSN. ADMC-MAC protocol is one of these latest developed protocols. In previous work, the protocol is tested for 40% duty cycle. This paper does the performance evaluation of this protocol in NS-2.35 for different missioncritical scenarios at different duty cycles for mission-critical performance parameters where the actual novelty lies. The results show that the proposed protocol gives best performance when set at 40% duty cycle. It is adaptive to the mission-critical applications and maintains the mission-critical data transport performance along with the energy of the wireless sensor networks. Simulation results confirm that ADMC-MAC model can further be enhanced to reduce data processing delay and for improving data delivery performance. The model may be enhanced by including intelligent nodes only for analyzing and data processing to reduce processing delay .The enhanced model suggested may be used for Mission-critical Wireless Sensor networks in integration with IoT for quick actions in mission-critical application.

Prospect of Natural Gas Distribution Pipelines and Safety in Tanzania-a case Study

Energy security is the first priority in any society worldwide. Effective distribution of clean and safe energy is still a challenge for researchers in the energy industry. This study aims to design a natural gas distribution network for supplying clean and safe natural gas for transport, residential, industrial and commercial needs. A case study in Tanzania, for the Tanzania Petroleum Development Corporation (TPDC) natural gas distribution pipeline. Effective pipeline network will reduce the consumption of electricity, coal, and charcoal. For safety purpose, the gas pipeline leak detection system is necessary, which is discussed and proposed in this paper. The old traditional method used for distributing gas energy to the domestic users in Tanzania is through liquefied petroleum gas (LPG) cylinders. This method has numerous drawbacks as cases of accidents are frequently reported.

Low-Cost, Tiny-Sized MEMS Hydrophone Sensor for Water Pipeline Leak Detection

In this paper, we present an experimental investigation of a water pipeline leak detection system based on a low-cost, tiny-sized hydrophone sensor fabricated using the microelectromechanical system (MEMS) technologies. A 10 × 10 element arrayed MEMS hydrophone device with chip size of 3.5 × 3.5 mm $^2$ was used in the experiment. The hydrophone device is packaged with a customized on-board preamplification circuit using an acoustic transparent material. The overall package size of the MEMS hydrophone is $\Phi$ 1.2 × 2.5 cm. The packaged MEMS hydrophone achieves an acoustic sensitivity of −180 dB (re: 1 V/ $\mu$ Pa), a bandwidth from 10 Hz to 8 kHz, and a noise resolution of around 60 dB (re: 1 $\mu \text{Pa/}\sqrt{\text{Hz}}$ ) at 1 kHz. A section of ductile iron water pipeline with an internal diameter of 10 cm, wall thickness of 0.73 cm, and length of 30 m is constructed as the test bed for the water leak detection. Two different leak sizes with leak flow rates of about 30 and 180 L/min are designed along the pipe, which is pressurized at 3.2 bar. Analysis of the transient signals and spectrograms shows that the MEMS hydrophone can capture the key acoustic information of the water leak, i.e., identifying the leak and locating the leak position. The measurement results demonstrate the feasibility to construct an affordable, highly efficient, real-time, and permanent in-pipe pipeline health monitoring network based on the MEMS hydrophones due to their high performance, low cost, and tiny size.

Pipeline Leak Detection Systems and Data Fusion: A Survey

The pipeline leakage problem is a very challenging and critical issue. Solving this problem will save the nation a lot of money, resources and more importantly, it will save the environment. This paper discusses the state-of-the-art of leak detection systems (LDSs) and data fusion approaches that are applicable to pipeline monitoring. A comparison of LDSs is performed based on well-defined criteria. We have classified and critically reviewed these techniques. A thorough analysis and comparison of all the recent works have been provided.

Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System

Data cannot be shared and leakage cannot be located simultaneously among multiple pipeline leak detection systems. Based on cyber-physical system (CPS) architecture, the method for locating leakage for pipelines with multibranch is proposed. The singular point of pressure signals at the ends of pipeline with multibranch is analyzed by wavelet packet analysis, so that the time feature samples could be established. Then, the Fischer-Burmeister function is introduced into the learning process of the twin support vector machine (TWSVM) in order to avoid the matrix inversion calculation, and the samples are input into the improved twin support vector machine (ITWSVM) to distinguish the pipeline leak location. The simulation results show that the proposed method is more effective than the back propagation (BP) neural networks, the radial basis function (RBF) neural networks, and the Lagrange twin support vector machine.

누설영역 분석을 이용한 배관 증기누설 위치 추정 방법

플랜트 배관의 누설감시 시스템은 누설 유무 판단뿐만 아니라 누설의 위치를 신속히 파악하는 것 또한 매우 중요하다. 플랜트 배관의 누설 검출 방법에는 주로 AE(acoustic emission)센서, 마이크로폰어레이 그리고 카메라 영상을 이용한 방법들이 있다. 최근 광역감시 및 원거리감시의 이점이 있는 카메라 영상을 이용한 방법에 대한 연구가 진행되어 왔다. 하지만 기존 카메라 영상을 이용한 방법들은 누설 유무와 대략적인 누설의 위치를 판단하고 있으나 누설이 시작되는 정확한 위치 추정에 대한 연구는 아직 미흡한 상태이다. 따라서 본 논문에서는 카메라를 이용한 누설 검출 방법을 이용해 누설영역을 산출하고 누설 검출 결과를 분석하여 누설 위치를 추정하는 방법을 제안하였으며 실험을 통하여 성능을 평가하였다. It is important to have a pipeline leak-detection system that determines the presence of a leak and quickly identifies its location. Current leak detection methods use a acoustic emission sensors, microphone arrays, and camera images. Recently, many researchers have been focusing on using cameras for detecting leaks. The advantage of this method is that it can survey a wide area and monitor a pipeline over a long distance. However, conventional methods using camera monitoring are unable to target an exact leak location. In this paper, we propose a method of detecting leak locations using leak-detection results combined with multi-frame analysis. The proposed method is verified by experiment.

Novel Negative Pressure Wave-Based Pipeline Leak Detection System Using Fiber Bragg Grating-Based Pressure Sensors

In this paper, the design and underpinning technical principles of the novel design of a negative pressure wave (NPW)-based pipeline leak detection system has been reported, which is configured using fiber Bragg grating (FBG) pressure sensors. To evaluate this, a pipeline leakage test platform has been established and experiments have been conducted, to verify the performance of a system using this FBG-based approach. The results show that a system using FBG-based sensors can accurately determine the pressure change trends along the pipeline and thus allow the calculation of the NPW velocity online. A key comparison is made with traditional NPW detection techniques, showing that the novel detection system is capable of achieving the higher leak-location accuracy and the detection of smaller leakage volumes. This arises from the ability of the FBG-based system to allow an increased number of sensors to be multiplexed along the pipeline. Their corresponding output signals generated show a very satisfactory, high signal-to-noise ratio. The system has been evaluated, especially in its response to extraneous signals and thus disturbances caused by the pump starting or stopping can be eliminated. This was achieved through an analysis of the time sequence of the pressure changes captured by the multisensor array being carried out and thus immunity to such effects demonstrated. The system has thus been designed to minimize the instances where a false alarm occurs.