Multiple Leakage Research Articles

SDCSN: A Hierarchical parallel Localisation Method for Pipeline Leakage Based on Vibration Signals

Abstract In applying deep learning methods to detecting and localising pipeline leaks, improving the fitness of deep learning methods to leak signals is an important task. We propose a novel detection model called Stacked dilated convolutional shrinkage network (SDCSN). This model incorporates a Stacked Dilated Convolution (SDC) module specifically designed for vibration signals, enabling the extraction of rich multi-scale local features. Moreover, implementing the Residual Shrinkage Building Unit (RSBU) module for noise reduction in the network architecture. Building upon this foundation, we introduce a new concept centred around hierarchical leakage discrimination and parallel prediction positioning. This approach enables accurate assessment of leakage levels and precise identification of multiple leakage points. Finally, the performance of the proposed method is verified in real experiments and the optimal settings for the dilated rate are determined. The results demonstrate a maximum classification accuracy rate reaching 98.94%.

Oil pipeline multiple leakage detection and localization based on sensor fusion

Oil pipeline multiple leakage detection and localization based on sensor fusion

Neural network-enhanced internal leakage analysis for efficient fault detection in heavy machinery hydraulic actuator cylinders

This work presents a method for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced into the hydraulic cylinder actuator, and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information such as location, height, and width. After the neural network has undergone the training process, it is deployed for the purpose of categorizing fault levels into three distinct categories: “a healthy system,”“a system with a low fault,” and “a system with a high fault.” The proposed technique utilizes pressure difference signals from the cylinder chambers and extracts features from the peak signals to reduce dimensionality. The extracted features are then used for supervised training of an artificial neural network using a feed-forward algorithm. The trained network is capable of predicting the leakage class of unknown datasets. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This study introduces a notably efficient approach, predicated on artificial neural networks, for the identification of internal leakage faults in hydraulic cylinders, with specific emphasis on faults arising from component wear and seal damage. The practical value of this research lies in its potential to significantly improve the reliability and efficiency of hydraulic systems used in heavy machinery. By utilizing neural networks for internal leakage detection, this research addresses a critical issue that can lead to costly downtime and maintenance in industrial operations.

Honey password vaults tolerating leakage of both personally identifiable information and passwords

Honey vaults are useful tools for password management. A vault usually contains usernames for each domain, and the corresponding passwords, encrypted with a master password chosen by the owner. By generating decoy vaults for incorrect master password attempts, honey vaults force attackers with the vault’s storage file to engage in online verification to distinguish the real vaults, thus thwarting offline guessing attacks. However, sophisticated attackers can acquire additional information, such as personally identifiable information (PII) and partial passwords contained within the vault from various data breaches. Since many users tend to incorporate PII in their passwords, attackers may utilize PII to distinguish the real vault. Furthermore, if attackers may learn partial passwords included in the real vault, it can exclude numerous decoy vaults without the need for online verification. Indeed, both leakages pose serious threats to the security of the existing honey vault schemes. In this paper, we explore two attack variants of the inspired attack scenario, where the attacker gains access to the vault’s storage file along with acquiring PII and partial passwords contained within the real vault, and design a new honey vault scheme. For security assurance, we prove that our scheme is secure against one of the aforementioned attack variants. Moreover, our experimental findings suggest enhancements in security against the other attack. In particular, to evaluate the security in multiple leakage cases where both the vault’s storage file and PII are leaked, we propose several new practical attacks (called PII-based attacks), building upon the existing practical attacks in the traditional single leakage case where only the vault’s storage file is compromised. Our experimental results demonstrate that certain PII-based attacks achieve a 63–70% accuracy in distinguishing the real vault from decoys in the best-performing honey vault scheme (Cheng et al. in Incrementally updateable honey password vaults, pp 857–874, 2021). Our scheme reduces these metrics to 41–50%, closely approaching the ideal value of 50%.

Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

Hydrogen fueled gas turbine is an efficient and environmentally friendly energy conversion equipment. However, it is prone to gas leakages from corrosion-prone components and pipe connections. In order to address gas leakage problems, source term estimation (STE) can be applied to estimate the location and intensity of the leakage sources. Traditional STE methods are based on an optimization procedure using an atmospheric transport and dispersion model, which requires considerable computation efforts and cannot meet the need of real-time implementation. The complex flow field around the gas turbine, the possible existence of multiple leakage sources, and the high-dimensional time series data further increase the difficulty of the STE problem. To address these challenges, in the present study, a deep learning based STE approach is proposed. The basic idea is to use long short-term memory auto-encoder (LSTM-AE) network to extract the encoded features of multi-sensor data and then use a deep neural network (NN) to establish the relationship between the encoded features and the source term parameters of hydrogen leakages. The multi-sensor data are generated using computational fluid dynamics (CFD) analysis to simulate relevant hydrogen leakage scenarios of concern. The results show that compared with other machine learning algorithms, the proposed approach has an overall best performance in terms of the STE accuracy. Specifically, its hydrogen leakage source localization accuracy is 0.9798, and the leakage strength estimation R-squared (R2) can reach 0.9632. When the training data proportion is only 20–30%, the proposed approach can still perform well, indicating the ability of the model to effectively predict the location and strength of the leakage source even with relatively less training data.

Research on acoustic localization method for multiple leakage sources of water-cooling wall in furnace

Abstract The water-cooling wall of the furnace often leaks under harsh conditions such as high temperature and pressure, which affects the safety and economic operation of the boiler. To achieve the localization of multiple leakage sources in water-cooling wall, an acoustic positioning method based on improved differential evolution and density-based spatial clustering of application with noise (IDE-DBSCAN) is proposed. The nonlinear equation system is constructed using time delay of arrival (TDOA) obtained by the generalized second cross-correlation algorithm, and the IDE algorithm is used to solve nonlinear equation system and obtain multiple spatial positions corresponding to multiple TDOA sequences. To eliminate false leakage sources, the paper presents IDE-DBSCAN to cluster the multiple estimated positions. The number of clusters obtained is the number of leakage sources, and the cluster centers are the location coordinates of the leakage sources. The numerical simulation results show that compared to traditional acoustic positioning methods, IDE-DBSCAN has higher positioning accuracy and better anti-noise ability. In addition, experiments are implemented on multiple leakage sound sources within a measurement area of 1m×1m×1.06m, and the positioning errors remain within 5cm. The proposed method provides a theoretical basis and experimental verification for further research on multiple leakages localization of water-cooling wall in furnace.

The method to improve the accuracy of experiment using helium instead of hydrogen

Based on the hydrogen-helium similarity theory, the leakage experiments using helium instead of hydrogen can effectively reduce the risk of hydrogen deflagration, but the experimental results show that there is a large difference between the concentration of hydrogen and helium in initial period of leakage base on this theory. This paper focuses on leakage experiments under turbulent conditions in enclosed spaces. Based on the hydrogen-helium similarity theory, helium is used to replace hydrogen to find out the concentration of helium at multiple leakage speeds. A comparison is made between helium concentration values and hydrogen concentration values, and a polynomial correction algorithm is used to shrink the difference. The results show that this correction method can reduce the concentration average error between the helium and the hydrogen from 14.06% to 7.94% for the Reynolds number range of 3500–9500. Therefore, a polynomial fitting algorithm based on this theory can improve the accuracy of experiment using helium instead of hydrogen.

Study on the characteristics of self-electric field of adjacent multiple leakage paths embankment dams and dikes under variable water levels

ABSTRACT The mastery of the distribution characteristics of the self-electric field of leaking embankment dams and dikes will impose positive effects on both detect and monitor leakage paths by self-potential method. We use numerical simulation and model experiment to investigate the self-electric field of embankment dams and dikes with adjacent multiple leakage paths under fluctuating water levels. The distribution characteristics of self-electric field in embankment dams and dikes with leakage paths at different spatial positions are researched by numerical simulation. The variation of self-potential data of horizontal and vertical survey lines on the surface are analysed. We establish a physical model and confirm that the numerical simulation results are reliable. The results show that as the water-level falls and rises, the self-potential of embankment dams and dikes correspondingly reduces and intensifies. During the water level’s descent, a low self-potential region appears above the leakage path’s exit, and the magnitude of the self-potential of this region is proportional to the leakage amount. The number and features of the spatial distribution of leakage paths within the dam are understood based on the peak self-potential of the vertical and horizontal survey lines. The findings will further promote the application of self-potential method.

Deep learning-assisted Bayesian framework for real-time CO2 leakage locating at geologic sequestration sites

Accurate and efficient localization of CO2 leakage if occurred in subsurface formations, is of significant importance in achieving secure geological carbon sequestration (GCS) projects. However, this task is inherently challenging due to the considerable uncertainties in the subsurface. In this work, we develop a novel deep learning-assisted Bayesian framework for identifying potential CO2 leakage sites based on the reservoir pressure transient behavior measured at the wellbores of injection or observation wells. The method consists of two essential steps: 1) Deep learning surrogate: This step aims to effectively replace the intensive high-fidelity simulation with an efficient deep learning surrogate. 2) Bayesian inversion: In this step, the posterior distributions of potential CO2 leakage locations are inverted, in which the surrogate serves as the forward model. The above two processes are automated using Bayesian optimization instead of a labor-intensive trial-and-error approach. The proposed framework is verified using a 3D geological model simulating CO2 sequestration into a brine-filled reservoir. The results demonstrate the Bayesian-optimized surrogate could successfully capture the underlying process of subsurface CO2-brine flow. The Bayesian inversion algorithm enables localizing CO2 leakage with high accuracy. To our knowledge, the proposed Bayesian framework is implemented for the first time to locate multiple leakage sites at the field scale. The proposed workflow provides an accurate and efficient approach to detecting possible CO2 leakage locations in a real-time manner and has promising potential for field-scale GCS applications.

Automatic location of multiple leakage points of hydropower station gate based on approximate kernel density estimation algorithm

Automatic location of multiple leakage points of hydropower station gate based on approximate kernel density estimation algorithm

Failure analysis on premature perforation of multiple effect evaporators in vacuum salt making system

Multiple effect evaporators (MEEs) are extensively utilized across diverse industries, such as pulp and paper, sugar, and desalination, for the purpose of concentration and purification. Nevertheless, MEEs often bring forth the issue of corrosion because of severe service conditions, thereby leading to equipment damage and decrease in efficiency. This study scrutinizes the premature perforation and corrosion of some multiple effect evaporators encountered soon after their commencement in a salt processing plant. The failed MEEs, designed to serve for 15 years, underwent severe corrosion and the emergence of multiple leakage points during a trial run of 40 days, with only 18 days of actual operation. To unearth the reasons for this failure, various macroscopic and microscopic analyses were carried out. The studies demonstrated that welding defects not found in time during the fabrication of evaporators reduced the corrosion resistance of the composite plate. Additionally, improper welding during the maintenance made the repair ineffective. Once the stainless-steel cladding layer was damaged, the carbon steel backing layer corroded and perforated rapidly under galvanic corrosion. Based on these findings, effective countermeasures were suggested to evade any future failures of the same kind. The outcomes of this analysis are likely to aid in enhancing the operational safety and reliability of multiple effect evaporators in vacuum salt-making systems.

Multi-Leakage Source Localization of Safety Valve Based on Uniform Circular AE Array and Improved MUSIC Algorithm.

The safety valve is the core component of the pressure-relief protection device for pressure-bearing special equipment. When the safety valve leaks, the medium of the pressure vessel will be lost and wasted, which may cause safety accidents. With the aim to solve the problem of accurately locating the multiple leakage sources of safety valves, a localization method combining a uniform circular array acoustic emission detection and an improved multiple signal classification (MUSIC) algorithm is proposed. First, an improved wavelet threshold function denoising method is introduced to extract acoustic emission signals with high SNR, thereby reducing the rank of the covariance matrix, weakening the noise dispersion caused by eigenvalue reconstruction, avoiding signal and noise cross-confusion, and improving positioning accuracy. By introducing a windowed fast Fourier transform (FFT) frequency division processing link to obtain narrowband signal, the premise of using MUSIC positioning algorithm is established. In addition, a forward/backward spatial smoothing algorithm is introduced in the decoherence link to reduce co-channel interference, reduce the rank loss of the signal covariance matrix, and improve the positioning accuracy of the algorithm. The results show that when the working pressure is 0.70 MPa, 0.75 MPa, and 0.80 MPa, the deviation between the azimuth angle and elevation angle positioning results of each leakage source obtained by the improved MUSIC algorithm and the actual angle does not exceed 2°, and the relative error does not exceed 3.5%. Therefore, the improved MUSIC algorithm can accurately locate multiple leakage sources of the safety valve, and as the working pressure of the safety valve increases, the positioning accuracy of the improved MUSIC algorithm also increases accordingly.

Fast Detection and Localization of Multiple Leaks in Water Distribution Network Jointly Driven by Simulation and Machine Learning

The leakage control in water distribution networks (WDNs) is of high concern in the water supply industry. One direct and effective way to reduce leakage is to adopt leakage detection and localization methods to guide water utilities to repair broken pipes in time. In order to achieve higher accuracy in the leakage detection process in WDN with multiple leaks, a novel multiple leak detection and localization framework (MLDLF) based on existing pressure and flow measurements is proposed. The MLDLF decomposed the problem into three substages: model calibration, leakage identification, and leakage localization. After using the calibrated hydraulic model to predict pressure values and estimate overall leakage flow in each area in the first stage, the data-driven methods, STLK, including the seasonal and trend decomposition using loess (STL decomposition) and the k-means clustering method, were performed in the identification stage to distinguish different leakage scenarios so as to determine the occurrence time of every leakage event. Finally, combined with the stepwise model–based fault diagnosis method, leakages were located gradually with high computational efficiency. A case study of applying MLDLF to the WDN of L-Town showed that 56.52% of the leakage events were successfully identified and located with the economic score reaching €264,873, indicating the robustness and good applicability of MLDLF in identifying and localizing all types of leaks under multiple leakage scenarios.

Autologous blood injection: alternative treatment for bleb leak

Background: To report a case of post-glaucoma drainage device (GDD) surgery with multiple small conjunctival defects treated with autologous blood injection.Case presentation: A 28-year-old female with a history of juvenile open-angle glaucoma who had undergone multiple glaucoma surgeries with antimetabolite injections for uncontrolled intraocular pressure (IOP) of the left eye since 2006 underwent Baerveldt glaucoma implantation in 2017. One year postoperatively, she experienced persistent hypotony, which could not be resolved with a scleral bandage contact lens. Ocular examination revealed visual acuity of 6/9 and IOP of 8 mmHg. Multiple leakage points were identified with fluorescein at the junction between the plate and the tube. Subconjunctival injection of 1 ml autologous blood was administered near the conjunctival defects. Five months postoperatively, the leakage was sealed with IOP of 12 mmHg.Conclusion: Autologous blood injection can be used as an alternative procedure for treating leaking blebs after a GDD surgery.

Multiple Leakage Detection and Isolation in District Metering Areas Using a Multistage Approach

Hydraulic accidents or abnormal situations, also known as leakages, cause not only water losses but also service interruptions and other negative effects. To facilitate the rapid response of water utilities and reduce water losses caused by undiscovered leakages, a timely, efficient, and accurate detection method is required. This paper describes a novel framework for multiple overlapping leak detection and isolation in the water distribution system. The proposed method has three stages: an estimation stage to extract leak-induced data from the huge monitoring data, an identification stage to detect the presence of overlapping leaks from multiscale difference series, and a localization stage to pinpoint the leak hotspots. The proposed method detects seven leakages in real-life networks and localizes 16 overlapping leakages with an economic score of €167,981 in L-town 2019. Comparisons show that the framework can accurately detect it in a fast response time and effectively localize the leaking pipeline with good applicability.

Computational methods for leakage localisation in a vacuum bag using volumetric flow rate measurements

The localisation of leakages during the vacuum bagging process of a composite is currently time intensive and reliant on human labour. The purpose of this article is to explore four computational methods for leakage localisation using volumetric flow rate (VFR) data to increase the automation of the process. The data used in this article is based on experiments on a square vacuum bag with four vacuum ports. The first method is based on potential flow theory to simulate the flow. In the second method, numerical regression was applied to find a relation between the VFRs and port-leakage distances. The third method consisted of calculating the fractional VFRs for every point on a grid and finding the grid point whose values most closely correspond to the observed fractional VFRs. The last method involved training a machine learning algorithm with experimental data. After the development of the methods, their performance was tested over the entire single-leakage dataset and compared. The results were in the same order of magnitude for all methods, with an area of 10^5 mm^2 for the 95% confidence interval and a distance of 10^2 mm for the average error. Further research is required for application to different vacuum bag shapes and multiple leakages cases. The method with the highest adaptability and performance is the method based on potential-flow theory. Machine learning and the potential-flow method is further applicable to multiple leakage localisation.

Multi-Leak Deep-Learning Side-Channel Analysis

Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES). By utilizing deep-learning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic algorithm. However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a appropriate preprocess step for each leakage interval. This degenerates the quality of profiling traces due to the noise and non-primary components. In this paper, we first divide the multi-leaky traces into leakage intervals and train models on different intervals separately. Afterwards, we concatenate these neural networks to build the final network, which is called multi-input model. We test the proposed multi-input model on traces captured from STM32F3 microcontroller implementations of AES-128 and show a 2-fold improvement over the previous single-input attacks.

Hybrid Dynamic Bayesian network method for performance analysis of safety barriers considering multi-maintenance strategies

Safety barriers play a critical role in preventing unintentional hydrocarbon flow leaking from reservoir to external environment or another formation during different offshore operation stages, and such a leakage has the potential to trigger cascading events and may lead to catastrophic consequences. The present study aims at the development of a hybrid DBN-based approach for dynamic performance analysis of safety barriers in the prevention of subsea downhole leakage incidents. Events in operation, such as different types of maintenances and process demand are taken into account to enhance the safety barrier performance. These factors could be analyzed by reflecting inspecting and repair activities of safety barriers with multistate-based multiphase Markov process. In order to obtain a dynamic and synthetic risk analysis of subsea downhole leakage, a dynamic Bayesian network-based model is proposed, incorporating the failure analysis of safety barriers and downhole multiple leakage pathways. Such analysis allows determining the dynamic risk characteristic of leakage events, and key safety barriers under different maintenance scenarios. Dynamic performance of such safety barriers is evaluated with respect to four aspects: preventive maintenance and imperfect repair, degradation effects, process demand and maintenance cost. The approach is tested through the application to a case study with an offshore oil and gas well. The results the importance of safety barrier performance in controlling the expected leakage scenarios.

Numerical estimation of multiple leakage positions of a marine pollutant using the adjoint marginal sensitivity method

When abnormally high concentration of a pollutant is observed in the ocean, it is very important to search for leakage positions and fluxes of the pollutant. To reduce time and expense of such search effort in the ocean, a numerical method was proposed for estimating a leakage position and its flux using a limited number of observed concentration data. This method is called the adjoint marginal sensitivity method, which is a time-backward probabilistic method to estimate a leakage position and a flux. In this study, we improved the method to deal with multiple leakage positions and validated this newly developed method. In addition, we showed the conditions for successful estimation using the simulation results of a well-planned two-dimensional case studies by changing flow velocity, eddy diffusion coefficient, distance between two leakage positions, spatial observation range, and sensor spacing. Moreover, we applied this method to a three-dimensional case with real topography and obtained reasonably acceptable results, suggesting that the knowledge learnt in the two-dimensional case studies is effective.

Method and Test Result Analysis of Well Integrity Detection in Deep Oil and Gas Wells

To solve the problems of multiple failed barriers, various failure types and significant difficulty to detect in deep oil and gas well is featured, a detection method is proposed based on available detection methods, related equipment and technology, which takes acoustic and electromagnetism as the core. This method can locate and identify tubing leakage, thread leakage, casing leakage, cement sheath channeling behind casing, leakage under liquid level and multiple leakage. In the field test, some other measures are adopted, including artificial pressure difference, connecting several noise devices, measurement at fixed point and controlling device speed. Two leakage points in the production string were successfully located and identified. The maximum depth was 6,330 m, the maximum temperature was 160 ℃, and the maximum pressure was 83 MPa. The research indicates that the proposed method and technology is suitable for the integrity detection of deep oil and gas wells, which can provide technological support for the well construction, completion and integrity management. Next step, the detection efficiency should be promoted and the interpretation charts should be enriched. Also, related regulations and standards should be developed. The prediction of well life and rigless well repair should also be studied, thus ensuring the safe and efficient development of deep oil and gas resources.