Pipeline Leak Detection Research Articles

Verification and evaluation methods for pipeline leak detection algorithms

Abstract As an important equipment in energy transportation and utilization, state detection is a key link to ensure the safety and environmental protection of the equipment. At present, there are many types of pipeline leakage detection algorithms, but there is less verification and evaluation of the actual performance and characteristics of the algorithm. Therefore, based on the evaluation index system, the entropy weight method (EWM), and the TOPSIS, we build a pipeline leakage inspection evaluation model of the measurement algorithm. In addition, the actual liquid pipeline transportation experiment platform is established. The negative pressure wave method is selected and combined with the CNN-LSTM algorithms to perform experiments. The results show that the algorithm used in this paper is better than the traditional negative pressure wave algorithm (NPW). The performance and calculation results can provide a reference for scientific research, engineering, teaching, etc.

Investigation of the kinematics behavior of spherical detector with different diameter ratios in oil pipelines

The spherical detector is an important technical equipment for water supply pipeline leakage detection. Its stable operation inside the pipeline will be affected by the fluid state in the pipeline, and the motion law of detectors with different diameters is different. In this paper, the dynamic analysis is carried out using the numerical simulation method, and the motion of spherical detectors with different diameters in horizontal oil pipelines is simulated. It is found that the flow state of the detector changes from the acceleration stage to the stable stage during the motion process, and with the decrease of the diameter ratio, the pressure difference between the front and back of the detector gradually increases, the thrust shows an upward trend, and the speed shows a greater speed in the stable stage. The motion behavior of the detector under different pipe diameter ratios is obtained, which provides help for the subsequent optimization of the shape design of the micro-leakage internal spherical detector.

Leakage detection of an acoustic emission pipeline based on an improved transformer network

Pipeline leakage detection is an integral part of pipeline integrity management. Combining AE (Acoustic Emission) with deep learning is currently the most commonly used method for pipeline leakage detection. However, this approach is usually applicable only to specific situations and requires powerful signal analysis and computational capabilities. To address these issues, this paper proposes an improved Transformer network model for diagnosing faults associated with abnormal working conditions in acoustic emission pipelines. First, the method utilizes the temporal properties of the GRU and the positional coding of the Transformer to capture and feature extract the data point sequence position information to suppress redundant information, and introduces the largest pooling layer into the Transformer model to alleviate the overfitting phenomenon. Second, while retaining the original attention learning mechanism and identity path in the original DRSN, a new soft threshold function is introduced to replace the ReLU activation function with a new threshold function, and a new soft threshold module and adaptive slope module are designed to construct the improved residual shrinkage unit (ASB-STRSBU), which is used to adaptively set the optimal threshold. Finally, pipeline leakage is classified. The experimental results show that the NDRSN model is able to make full use of global and local information when considering leakage signals and can automatically learn and acquire the important parameters of the input features in the spatial and channel domains. By optimizing the GRU improved Transformer network recognition model, the method significantly reduces the model training time and computational resource consumption while maintaining high leakage recognition accuracy. The average accuracy reached 93.97%. This indicates that the method has good robustness in acoustic emission pipeline leakage detection.

A novel percussion-based approach for pipeline leakage detection with improved MobileNetV2

Pipelines are susceptible to oil and gas leaks during long-distance transportation due to factors such as damage from external forces and aging. However, existing pipeline leakage detection technologies that rely on physical inspections or sensors installed on pipelines are time-consuming and costly. In this paper, a percussion approach based on improved MobileNetV2 is proposed for pipeline leakage detection. Firstly, the influence of pipe leakage size on vibration characteristics was investigated by theoretical analysis and numerical simulation. Subsequently, experiments were conducted to assess the validity of the proposed method. The sounds produced by hammering the pipe under different damage conditions were recorded using a smartphone. The improved MobileNetV2 model was then used for classifying Mel spectrogram and Mel frequency cepstrum coefficient (MFCC) features extracted from the recorded sound signals. This model incorporates a multi-scale feature fusion module, which allows it to capture features at different scales and enhances its ability to differentiate between damage conditions. Experimental results show that using Mel spectrogram as input for the improved MobileNetV2 achieves a higher accuracy compared to using MFCC, with 100% accuracy for identifying leakage damage and 99.87% for classifying leak size. Compared to other methods, the improved MobileNetV2 exhibits superior classification performance while maintaining the lightweight characteristics of the original MobileNetV2. In conclusion, the improved model demonstrates significant enhancements in classification performance and operational efficiency, making it a promising approach for processing percussive signals.

A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

Water Pipeline Leakage Detection Based on Coherent φ-OTDR and Deep Learning Technology

Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.

Study on vibration signals identification method for pipeline leakage detection based on deep learning technology

For natural gas pipeline leakage systems based on distributed feedback fiber laser vibration sensors (DFB-FL), a novel neural network model (mCNN-LFLBs) integrating multi-scale convolutional neural networks (mCNN) with local feature extraction blocks (LFLBs) is proposed in this study. To address the problem of feature loss in the traditional signal image transfer process, the one-dimensional original signal is directly used as the input of the model, effectively maintaining the time-domain correlation of the leaked signal. The time-domain correlation of the leakage signal is preserved. To address the problem that traditional pattern recognition algorithms have a weak ability to extract deep features and local features, mCNN is established by this model, which realizes the use of convolution kernels of different scales to extract features of different frequency bands in one-dimensional signals. Additionally, LFLBs are introduced, which enhances the local expression ability of the model, further improves the feature mining quality of the model, and better presents the deep features of leaked signals. Experimental results demonstrate that the mCNN-LFLBs model has a classification accuracy of up to 98.6%, with a testing response time of approximately 10 ms, and outstanding performance in terms of convergence speed. These results highlight the potential practical application of the mCNN-LFLBs model in natural gas leak detection.

Roadmap for Recommended Guidelines of Leak Detection of Subsea Pipelines

The leak of hydrocarbon-carrying pipelines represents a serious incident, and if it is in a gas line, the economic exposure would be significant due to the high cost of lost or deferred hydrocarbon production. In addition, the leakage of hydrocarbon could pose risks to human life, have an impact on the environment, and could cause an image loss for the operating company. Pipelines are designed to operate at full capacity under steady-state flow conditions. Normal operations may involve day-to-day transients such as the operations of pumps, valves, and changes in production/delivery rates. The basic leak detection problem is to distinguish between the normal operational transients and the occurrence of non-typical process conditions that would indicate a leak. To date, the industry has concentrated on a single-phase flow, primarily of oil, gas, and ethylene. The application of a leak-monitoring system to a particular pipeline system depends on environmental issues, regulatory imperatives, loss prevention of the operating company, and safety policy rather than pipe size and configuration. This paper provides a review of the recommended guidance for leak detection of subsea pipelines in the context of pipeline integrity management. The paper also presents a review of the capability and application of various leak detection techniques that can be used to offer a roadmap to potential users of the leak detection systems.

Data-Driven Inline Leak Detection for Pipelines Using Flow-Induced Acoustics Analysis

Fluid and water distribution networks are essential to the modern world. However, these systems are prone to leaks, which can lead to significant water loss, damage to infrastructure, and environmental pollution. The proposed solution makes use of Acoustic Emission sensors placed in discrete locations in the pipeline which measures the sound in the pipeline caused by the flow of fluids. Computation models are used to deduce the location from the input provided by the sensors. In case of leak, the leak is localized through cross correlation and TDOA methods. This solution is particularly developed for water distribution pipelines. Keyword: Acoustic data analysis, Data-driven models, Cross-Correlation, Time Difference of Arrival (TDOA)

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

In this paper, the possibility of using monofractal and multifractal analysis of acoustic signals of pipelines to detect leaks is considered. An experimental stand has been created to study the fractal characteristics of acoustic signals of pipelines with “slit” type defects. During the experiments, defects of the “slit” type pipeline with dimensions of 2 mm, 8 mm, and 20 mm were modeled. Detrended fluctuation analysis (DFA) and the multifractal detrended fluctuation analysis (MF-DFA) were used. As a result of the experimental studies, it was found that the occurrence of leakage leads to the occurrence of anticorrelated vibrations in a pipeline with multifractal properties. The analyses of acoustic signals by DFA and MF-DFA methods make it possible to reliably determine the leakage. The Hurst exponent and the width of the multifractal spectrum can serve as indicators of the occurrence of leaks in pipelines.

Development of a Conservative Hamiltonian Dynamic System for the Early Detection of Leaks in Pressurized Pipelines

In this study, we propose an innovative approach for real-time leakage detection in pipelines by integrating conservative Hamiltonian equations and experimental Internet of Things (IoT) technologies. The proposed method combines a hybrid model that utilizes sensors and IoT devices to acquire real-time data and solves the coupled system of Hamiltonian equations using the ODE45 numerical integration method. Spectral frequency analysis is an essential part of this method, as it reveals specific patterns in the pressure and flow signals. The findings highlighted 95% accuracy in leak detection, which was validated through a comparison of the theoretical and experimental data. The novelty of this approach lies in its ability to maintain constant total system energy, thereby enabling continuous monitoring for early leak detection. As an improvement, the proper handling of sensor signals is emphasized, underscoring its contribution to the efficient management of water resources in potable water distribution systems. Doi: 10.28991/CEJ-2024-010-04-01 Full Text: PDF

ECNet: Network Based on Encoder With Local Convolutional Attention and Cross Layer Information Fusion for Natural Gas Pipeline Leakage Detection

ECNet: Network Based on Encoder With Local Convolutional Attention and Cross Layer Information Fusion for Natural Gas Pipeline Leakage Detection

Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm

Oil and Gas Pipeline Leakage Detection using IoT and Deep Learning Algorithm

Oil Pipeline Leak Detection in Iraqi Oil Fields based on 1DCNN

The oil industry plays a crucial role in Iraq's economy. There's a growing need for technologies that can quickly detect leaks in oil pipelines because leaks can have serious ramifications, including monetary losses, endangerment to public safety, environmental degradation, and resource waste. Advances in technology and software have made it possible to detect leaks. Current approaches often require manual extraction of features, which can be slow and inefficient. This paper presents a new method that proposes using convolutional neural networks (CNNs) for automatic feature extraction. The Iraqi Ministry of Oil, specifically the Basra Oil Company, provided the dataset, such as total distance (km), pressure (bar), and flow rate (STB/d). We split the data into training (70%) and testing (30%) sets. then we calculate metrics such as confusion matrices, accuracy, precision, recall, and F-score to evaluate performance and calculate errors from the regression analysis (root mean square error, root mean absolute error, and relative error). Our contribution to this work is to use 1DCNN to identify leaks, pinpoint their location, and even predict the amount of spilled oil, unlike other research that only uses it to evaluate the presence or absence of a leak only. Additionally, we've created a user-friendly interface for the system. Finally, compare the proposed approach with conventional and alternative methods to show its efficiency. In the future, we plan to expand the system to assess pipeline corrosion and predict its remaining lifespan.

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural stresses by analyzing the hyperspectral reflectance of vegetation. The effectiveness of this discrimination is assessed across three distinct spectral ranges (VNIR, SWIR, and Full spectra). Greenhouse experiments subjected three plant species to controlled environmental stressors, including gas leakage, salinity impact, heavy-metal contamination, and drought exposure. Spectral curves obtained from the experiments underwent preprocessing techniques such as standard normal variate, first-order derivative, and second-order derivative. Principal component analysis was then employed to reduce dimensionality in the spectral feature space, facilitating input for linear/quadratic discriminant analysis (LDA/QDA) to identify and discriminate gas leaks. Results demonstrate an average accuracy of 80% in identifying gas-stressed plants from unstressed ones using LDA. Gas leakage can be discriminated from scenarios involving a single distracting stressor with an accuracy ranging from 76.4% to 84.6%, with drought treatment proving the most successful. Notably, first-order derivative processing of VNIR spectra yields the highest accuracy in gas leakage detection.

Two-dimensional small leak detection of pipeline based on time sequence coding

In this study, a two-dimensional pipeline small leak detection based on time series imaging is introduced. First, the sampling points and data of pipeline signals are converted into polar diameters and polar angles in polar coordinate system to form vectors, and then the inner product between vectors is mapped to 2-dimensional pixels through the use of Gram angular field (GAF). The resulting image preserves both the temporal and spatial features of the original signal. Wavelet threshold (WT) denoising and adaptive segmentation constant approximation (APCA) are used to denoise and fit the original signal in order to ensure the noise immunity and computational efficiency of the imaging. The image generated after WT-APCA-GAF processing is input into Swin Transformer for training, and the final identification accuracy of small leakage can reach 100%, effectively avoiding the problem of missing and false positive in detecting small leaks.

EMDet: An entropy blending and multi-link parallel feature enhancement detection model for gas pipeline weak leakage detection

To improve the detection accuracy of gas pipeline leakage detection (PLD) especially when the leakage is weak, we propose a hybrid deep learning model, named EMDet in this paper, which incorporates two proposed algorithms called entropy blending (EB) and multi-link parallel feature enhancement (MPFE). Firstly, the EB algorithm is used to determine the optimal number of decomposition layer Lo and the optimal intrinsic mode function Io for variational mode decomposition (VMD), elevating the leakage-related information in Io. Then, three coarse-grained scales have been applied to extract effective and robust features, where the coarse-grained scales are calculated based on the sizes required by the MPFE algorithm. Moreover, the MPFE algorithm is proposed to enhance the representation of features, thereby improving the detection accuracy of PLD. After that, the performance of the proposed EMDet has been evaluated on the negative pressure wave (NPW) data collected from realistic urban environments. Compared with the state-of-the-art PLD models, the performance of EMDet not only reaches the total accuracy of 98.67%, the F1-score of 98.65%, and the fault detection rate of 98.68%, but also reduces the false alarm rate to 0.66% and the missing alarm rate to 1.32%. Finally, the robustness results demonstrate the superior performance and broad application prospects of EMDet for PLD.

Energy-efficient routing protocol for reliable low-latency Internet of Things in oil and gas pipeline monitoring.

The oil and gas industries (OGI) are the primary global energy source, with pipelines as vital components for OGI transportation. However, pipeline leaks pose significant risks, including fires, injuries, environmental harm, and property damage. Therefore, maintaining an effective pipeline maintenance system is critical for ensuring a safe and sustainable energy supply. The Internet of Things (IoT) has emerged as a cutting-edge technology for efficient OGI pipeline leak detection. However, deploying IoT in OGI monitoring faces significant challenges due to hazardous environments and limited communication infrastructure. Energy efficiency and fault tolerance, typical IoT concerns, gain heightened importance in the OGI context. In OGI monitoring, IoT devices are linearly deployed with no alternative communication mechanism available along OGI pipelines. Thus, the absence of both communication routes can disrupt crucial data transmission. Therefore, ensuring energy-efficient and fault-tolerant communication for OGI data is paramount. Critical data needs to reach the control center on time for faster actions to avoid loss. Low latency communication for critical data is another challenge of the OGI monitoring environment. Moreover, IoT devices gather a plethora of OGI parameter data including redundant values that hold no relevance for transmission to the control center. Thus, optimizing data transmission is essential to conserve energy in OGI monitoring. This article presents the Priority-Based, Energy-Efficient, and Optimal Data Routing Protocol (PO-IMRP) to tackle these challenges. The energy model and congestion control mechanism optimize data packets for an energy-efficient and congestion-free network. In PO-IMRP, nodes are aware of their energy status and communicate node's depletion status timely for network robustness. Priority-based routing selects low-latency routes for critical data to avoid OGI losses. Comparative analysis against linear LEACH highlights PO-IMRP's superior performance in terms of total packet transmission by completing fewer rounds with more packet's transmissions, attributed to the packet optimization technique implemented at each hop, which helps mitigate network congestion. MATLAB simulations affirm the effectiveness of the protocol in terms of energy efficiency, fault-tolerance, and low latency communication.

Development and Evaluation of a MQ-5 Sensor-Based Condition Monitoring System for In-Situ Pipeline Leak Detection

The condition monitoring system for an in-situ pipeline is an innovative concept that uses MQ-5 sensors to detect fuel leaks in pipelines and relay concentration data to a receiver station. The essence of the monitoring process is to ensure the safety and security of engineering properties and lives. The project addresses the crucial requirement for rapid detection of fuel leaks to avoid environmental problems, economic losses, and safety risks connected with pipeline circulation. The goal is to create a leak detection monitoring system. The project required the design and execution of four transmitter stations, each responsible for detecting fuel leaks at various places along a small-scale pipeline and transmitting concentration data to a central receiver station. The system's response time, a critical performance parameter for this project, was measured by timing how long it took for an alert to arrive at the receiving station after the transmitter detected a leak. This time was measured at three distances between the transmitter and receiver stations: 1m, 2m, and 3m. Multiple measurements were taken at each distance, and the average response time was computed. The results showed that as the distance between the stations increased, so did the reaction time. The average response time at 1m was 3.37 seconds, whereas, at 2m and 3m, it was 3.856 seconds and 4.198 seconds, respectively. A t-test inference was performed to check that there was a distinct difference between the response times for each distance, and a significant difference was detected. The built system effectively demonstrated its ability to detect fuel leaks, and the observed response times offered useful information about the system's performance at various distances. This technology demonstrates the potential to improve pipeline transportation safety and efficiency by enabling early identification of fuel leaks.

Online fault detection and localization of multiple oil pipeline leaks using model-based residual generation and friction identification

Online fault detection and localization of multiple oil pipeline leaks using model-based residual generation and friction identification