Leak detection and localization for pipelines using multivariable fuzzy learning backstepping

The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized cross-correlation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location.

Digital twins are transforming the digitalization and automation of offshore gas pipeline systems by enabling realtime monitoring, predictive maintenance, and operational efficiency. This study introduces a novel adaptive digital twin framework designed for leak detection and localization in offshore gas pipelines. The framework integrates OLGA-generated synthetic data, validated experimental results, and advanced machine learning (ML) techniques, including transfer learning and ensemble models. The proposed framework achieves a classification accuracy of 98.2% for leak detection, with a mean absolute error (MAE) of 0.11 cm for leak size prediction and a mean absolute percentage error (MAPE) of 3.8% for leak localization. A core innovation of this framework is the calibration methodology, which recalibrates dimensionless nomographs and leak detection correlations for seamless adaptation to new pipeline geometries and operating conditions. Through systematic steps, the calibrated correlations predict leak size and location with high accuracy, leveraging pressure drop and mass flow difference data. Additionally, ML-driven models enable efficient generation of new nomographs for pipelines with varying configurations, enhancing scalability and reducing computational effort. The real-time implementation enables predictions with a latency of less than 2 seconds, significantly outperforming conventional methods in speed and accuracy. Also, the framework’s adaptability, supported by its digital twin visualization and real-time feedback mechanisms, significantly improves pipeline integrity management, operational safety, and environmental protection. The study demonstrates the framework’s robustness in handling complex flow dynamics and offers a scalable solution to enhance the digital transformation of offshore oil and gas operations.

This paper presents a methodology for leak detection and location in a great longitude pipeline, which belongs to E.P. PETROECUADOR, through the combination of line balance and speed propagation of the negative pressure wave. Two different approaches, slope projection and a first order dead time (FODT) model, are used for computing the pressure change time, which in combination with speed propagation of the sound is used for estimating leak location. A better performance in leak location accuracy than a commercial installed system is obtained, whose results are compared.

The commonly used cross-correlation technique for leak location requires that the acoustic speed is known and invariable. In practice, the gas leakage-induced acoustic waves propagate along multiple paths including in-pipe gas and pipe wall, and the acoustic waves in different transmission paths exhibit different acoustic speeds and different dispersive behaviors, which bring a great challenge for leak detection and location in the gas pipelines. In this study, based on the vibration theory of cylindrical elastic thin shell, the wavenumber formulae in different transmission paths are derived to predict the acoustic speeds and the acoustical coupling between the in-pipe gas and the pipe wall is analyzed to determine the dominant transmission path. In addition, the velocity dispersions in the dominant transmission path are suppressed by selection of a characteristic frequency band of the gas leakage-induced acoustic waves. The theoretical predictions are verified in the experiment and the results show that the theoretical acoustic speed is slightly larger than the measured acoustic speed. Thus, the theoretical acoustic speed formula is modified considering the effect of the structural loss factor and consequently the location error using the modified acoustic speed is reduced by two times compared to that using the theoretical acoustic speed.

Prompt and reliable detection of pipeline leaks is vital for human safety, the economy, the environment, and corporate reputation. However, a simple mechanistic model for accurately predicting leak characteristics in different flow regimes is lacking. To fill this gap, a novel methodology was used to develop a multiphase flow leak detection model using only inlet and outlet parameters. The gas-liquid two-phase leak mass flow rate, location, and size are computed through iterative processes in the upstream and downstream sections of the leak. Data sets were generated for a wide range of geometric (3 – 5 inch pipe diameter, 2000 – 10000 feet pipe length, 500 – 1500 feet leak location, 0.2 – 3 inch leak opening diameter), hydrodynamic (Newtonian, air, CO2, N2) and operating conditions (0.3 – 0.628 outlet liquid fraction). These data sets were utilized to develop contour plots and a data-driven model using statistical analysis based solely on the inlet and outlet parameters. The results indicate that a change in total mass flow rate and pressure in the inlet and the outlet section of the leak can be a good indicator for determining the location and size of the leak. The effect of different pressure constraints, pipe length, pipe diameter, two-phase fluid rheology, leak diameter, leak location, outlet liquid volume fraction, and flowing liquid hold-up on leak size, pressure, and flow rate is analyzed. Decreasing the liquid fraction in the outlet section of the leak leads to a slight increase (6% average) in the inlet mass flow rate and a significant decrease (50% average) in the outlet mass flow rate for fixed pressure constraints, resulting in an increased leak flow rate, pressure, and density. Similarly, longer pipe lengths, bigger pipe diameters, heavier gas phase, and lower liquid fraction at the outlet have higher leak pressure for the same leak locations due to higher leak flow rate. Furthermore, contour plots revealed that identifying a leak near the pipe inlet is easier, although determining its size remains challenging. On the other hand, detecting a leak near the pipe outlet is more difficult, but assessing its size is comparatively easier. The developed data-driven showed good agreement with different literature data sets with a MAPE of less than 20%. The mechanistic model's key advantage lies in its reliance on fundamental equations and physics, making it applicable to various operating conditions for field applications. Moreover, the data-driven model is straightforward and accurate, eliminating the need for complex simulations. This study has the potential to assist industries in determining leak location, size, and pressure using only the inlet and outlet parameters, without requiring multiple sensors along pipelines.

Pipeline leak detection has always been a relatively difficult technical problem; especially in urban pipeline leak detection, there are still many problems to be solved. A multipoint leak detection and location method for urban gas pipelines based on variational mode decomposition and relative entropy was proposed. Firstly, the experiment pipeline system was built, and the original signal was collected by acoustic emission technology; then, a variational model method was used to decompose the signal to obtain multiple intrinsic mode function (IMF) components with different characteristic scales. According to the characteristics of relative entropy, each component was analyzed, the appropriate IMF component was selected, and the selected component was reconstructed to obtain the observation signal. The multipoint leakage location model of the urban gas pipeline was established. The number of source signals was estimated by singular value decomposition, and the leakage signals were separated; finally, the accurate location of leakage point was achieved by cross-correlation positioning. The results showed that the average relative error of the pipeline leak location results is 2.97%, and the leak location accuracy is significantly improved, achieving the purpose of precise location.

Pipelines are a safe energy-efficient mode of transporting fluids and gases. However, risks of pipe leaks still exist. Various pipe leak detection methods are in use; some traditional methods rely on acoustic receivers that must be located on the pipe, thereby restricting their implementation. Microseismic monitoring resolves this restriction because the ground rather than the pipe is monitored. However, microseismic monitoring has, thus far, only been applied using 2D methods for pipeline leak detection, and its accuracy is limited. To address this problem, a new 3D leak detection and location method for buried pipelines is proposed. First, using a 3D staggered-grid finite-difference method for acoustic equations, we simulated a microseismic wave field generated by a leaking pipeline, and recorded it with a ground surface geophone array. Then, a generalized cross-correlation technique was used to extract the time difference. Finally, the simulated annealing method was used to obtain the leak location in 3D space. Simulation and field data results show that the proposed method can locate the leakage point and has good anti-noise capability. Accurate 3D leak location requires at least two survey lines. Non-parallel survey lines can locate the approximate plane position of the leakage. Parallel line and orthogonal line pairs can be used to obtain the leak position accurately; the parallel line pair is the most accurate. The proposed method was concluded to be effective and accurate at detecting and locating leakages in buried pipeline networks.

Pipeline leak detection using an acoustic emission (AE) method requires highly sensitive transducers responding to less attenuative and dispersive wave motion in order to place the discrete transducer spacing in an acceptable approach. In this paper, a new piezoelectric transducer geometry made of PZT-5A is introduced to increase the transducer sensitivity to the tangential direction. The finite element analysis of the transducer geometry is modeled in the frequency domain to identify the resonant frequency, targeting 60 kHz, and the loss factor. The numerical results are compared with the electromechanical characterization tests. The transducer response to wave motion generated in different directions is studied using a multiphysics model that couples mechanical and electrical responses of structural and piezoelectric properties. The directional dependence and the sensitivity of the transducer response are identified using the laser-induced load function. The transducer response is compared with a conventional thickness mode AE transducer under simulations and leak localization in a laboratory scale steel pipe.

Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil stolen every year results in huge economic losses on oilfield. Therefore, a real-time and accurate pipeline leak detection and location system not only can effectively decrease leakage loss and reduce the waste of manpower and material resources in patrolling work, but also is conductive to the management of oil pipeline and improvement of economic efficiency of enterprise. The paper determines leak detection and location project giving priority to negative pressure wave and supplemented by flow parameter analysis. The method not only can judge the accidence of leakage timely and accurately, but also can effectively avoid leakage false alarm caused by start or stop pumps in pipeline. Negative pressure wave method is the method which is used widely on pipeline leakage detection and location internationally at present. It is based on signal processing. And the method doesn't need to establish hydraulic and mathematical model of pipeline, and most only use pressure signal. So the method adapts to the application of pipeline in China(1). Because oil pipeline has the function of guiding wave, the negative pressure wave caused by leakage can transmit more than tens of kilometers along the pipeline. Its propagation velocity is equal to the propagation velocity of sound wave transferring fluid in pipeline. And the propagation velocity of negative pressure wave in the original oil pipeline is between 1000meters and 1200 meters. As negative pressure wave has the characteristics of remote transmission, pressure transducer installed at the end and first of the pipeline can be used to capture negative pressure wave containing leakage information and to detect the leakage. After identifying leakage happens, leakage location can be made according to time difference of negative pressure wave caused by leakage transmitting to the head and end of the pipeline and negative pressure wave velocity(2).

A Particle Filter Based Leak Detection Technique for Water Distribution Systems

In this paper, two machine learning techniques are applied and compared in order to model leak detection in pipelines in noisy environments. A set of accelerometers, mounted on the surface of the pipes, was deployed for the data acquisition process. Measurements of noise during normal operating conditions were recorded as well as measurements of leaks, generated on various distances from the sensors. Using these measurement data, a training set was created from their time-domain and frequency-domain features. The leak detection process is then modeled as a binary classification problem (leak detection or not). For this problem, two machine learning classification techniques were evaluated, the support vector machines and the decision trees. The results for each learner are compared with the original data from the test dataset using representative performance indicators and, overall, high levels of accuracy are achieved.

There is an increasing need for timely pipeline leak detection and localization methods, pipeline leak could lead to not only the loss of the goods but also considerable environmental and economic problems. With the rapid development of hardware and software, the pipeline leak detection and localization algorithms have been widely researched and applied in many Fields. However, traditional methods are usually limited by extracting features manually, which is inefficient and time-consuming. Convolutional neuron network is an effective method to extract features automatically. In this paper, a novel ensemble transfer learning one-dimension convolutional neural network (TL1DCNN) for the pipeline leak detection and localization is proposed. The TL1DCNN plays the role of base learner. The results of a set of obtained base learners are integrated to achieve the task of pipeline leak detection and localization. Firstly, one-dimension convolutional neural network (1DCNN) models with different parameters are pretrained with source domain data. A small learning rate is set to retrain the above 1DCNN models for target task with target domain data in order to obtain TL1DCNN base learners. Then, the four ensemble strategies with different number base learners whose ensemble weights are optimized by particle swarm optimization algorithm are obtained by minimizing the sum of similarity. The dataset simulated by pipeline network model is used to evaluate the effectiveness of the proposed approach. The indicators such as classification accuracy, precision, recall, F_score and confusion matrix are used to compare the proposed approach with traditional methods and other deep learning methods. The experimental results show that the performance of the proposed approach is superior to other compared methods.

Accurate leak localization within district heating networks (DHNs) is a significant challenge due to the complex hydraulic dynamics that govern these systems.Traditional methods for leak detection, such as manual inspections and thermal imaging, are often inefficient for real-time applications.Recent advancements in artificial intelligence (AI), particularly artificial neural networks (ANNs), offer promising solutions by analysing pressure and temperature variations in DHN pipelines to identify leak locations.This paper explores the potential of AI-driven techniques for improving leak localization accuracy within DHNs.Using synthetic data generated with a Modelica-based simulation of DHN conditions in Grenoble, France, the study evaluates the impact of pressure and temperature sensors installed in DHN.The model was developed and trained using multi-class classification techniques, with the dataset balanced via Random Under Sampling (RUS) to address class imbalances.Feature selection was performed using Random Forest to identify the most critical input features, which were then used in the ANN for leak localization.The results demonstrate the effectiveness of pressure sensors, particularly in the return lines, for enhancing leak localization precision, while temperature sensors, though less directly indicative of leaks, also contribute valuable insights.The study concludes that AI-based approaches, coupled with strategically placed sensors, can significantly improve the accuracy and efficiency of leak localization in DHNs, contributing to more effective predictive maintenance and reduced system downtime.

Pipeline networks are essential in conveying water, oil, gases as well as other es sential commodities in society. These networks must be protected from compro mise to prevent losses and possible negative impacts on the environment. This paper develops an advanced pipeline leak detection procedure that is optimized by way of Dense Hebbian Neural Networks (DHNNs). The implementation of the proposed system aims at using the novel neural networks’ structure in conjunc tion with signal recognition methods for piping leakage detection and localiza tion. The high denseness of interconnection in the DHNN helps in learning and offers simplicity in computation. The method used involves pressure and flow in formation at run time, whereby deviation from the norm is established and grouped according to patterns of the pipeline network. A comparison between the developed DHNN model and a Feedforward Neural Network (FNN) model is made to compare the increased leak detection efficacy, precision, and perfor mance measures. This paper uses basic mathematical models to assess signal behavior when different leak conditions are present and simulation results show the stability of the proposed solution. The simulation uses control variables cre ated within the MATLAB environment and includes realistic numerical pipeline data. Comparing DHNN with FNN, the findings reveal better precision, faster convergence rate, and better leak localization with the use of DHNN. Experi mental results are shown in figures and graphs which provide a clear comparison of the results obtained. The outcome of this research work is expected to improve the safety of pipelines and minimize preventable economic losses because of leaks, particularly through improving leak detection with modern concepts in neural network paradigms.

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification have been challenges. In this paper, an integrated approach to geospatial and infrared image processing was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by the Sharjah Electricity, Water, and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used to pinpoint the leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.