Identification Of Leaks Research Articles

Green Smart Technology for Water (GST4Water): Water Loss Identification at User Level by Using Smart Metering Systems

A real-time household water consumption monitoring and processing system aimed at leakage identification at user level is presented here. The system, developed within the GST4Water project, allows consumption data sent by a generic smart meter installed in a user’s house to be received and transferred to a cloud platform. Here, the consumption data are stored and processed through an empirical algorithm able to automatically identify leakage at the individual user level by looking for non-consumption in certain periods of the day. With reference to a real-life case study, the results obtained show that the algorithm enables leakages on users’ properties to be identified with an accuracy of more than 90%. Therefore, the implementation of this algorithm within a highly innovative smart metering system can represent an efficient tool for reducing water losses at user level.

Leakage Identification in Water Distribution Networks with Error Tolerance Capability

Leakages in water distribution networks have caused considerable waste of water resources. Thus, this study proposes a novel method for hydraulically monitoring and identifying regions where leakages occur in near-real time. A large network is first divided into several identification regions. To exploit a strong constructive and discriminative power, sparse coding is used, thereby adaptively coding the information embedded in observed pressures efficiently and succinctly. And a linear classifier is trained to determine the most likely leakage regions. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. Results indicate that the proposed method can identify leakage events with enhanced tolerance capability for measurement errors. The method is also partially effective for identifying two simultaneous leakages. Certain practical advice in balancing the number of sensors and regions is also discussed to enhance the application potential of this method.

Leak localization using empirical mode decomposition and Teiger-Kaiser energy operator analysis based on pressure transient signal: experimental study

Leak detection become crucial part in water management services due to strenuous work in identification of leak location for pipeline networks. This paper focused on leak identification and localization using Teiger-Kaiser Energy Operator (TKEO) as instantaneous frequency analysis (IFA) while Empirical Mode Decomposition (EMD) as decomposition method with implementation of Integrated Kurtosis Algorithm for Z-Filter (Ikaz) to Kurtosis ratio as the automatic selection criterion for intrinsic mode function (IMF). Test rig construct inside laboratory as testing site using 67.90-metre Medium Density Polyethylene (MDPE) pipe. In order to create an artificial leak, pinhole is drill at 19.75-metre distance from point of analysis that is fire hydrant attached with pressure sensor. Experiment conduct by using two variation of pressure that are 2bar and 4bar. As the result, with percentage of error less than 6%, combination of TKEO as IFA and efficiency of Ikaz performed well in locating the position of leak and outlet of pipeline system.

SIMULATION OF HYDROCARBON PIPELINE LEAK DETECTION USING THEORETICALLY APPROXIMATED MODEL ON A TRANSIENT-BASED METHOD

Detection and localization of small leaks in pipelines were early developed using a nonlinear adaptive state observer and a special correlation technique, based on pressure and flow measurements at the pipeline inlet and outlet. Simulations and experiments show the results for a gas and a liquid pipeline. This paper x-ray pipeline leak Identification using a transient-based method on hydraulic transients of a brake system with oil at two different temperatures, Leak effects at different frequency response were Simulated, measured and leak detection experiment with MATLAB code were also highlighted. The result obtained show that in certain circumstances the brake response is slow due to the temperature and suggests an appropriate pipe diameter to overcome this problem of low flow rate, the transfer function obtained using the proposed method were able to measure the presence of a leak on the pipe system. Simulations, measurements and leak experiments have shown that the early detection and localization of small leaks in liquid and gas pipelines can be considerably improved using theoretically approximated model as the required computational effort is relatively small, as compare to microcomputers which can be used for leak detection including monitoring of the inner pipeline state.

Quantifying Geological CO2 Storage Security to Deliver on Climate Mitigation

The Storage Security Calculator (SSC) is a tool to simulate the long-term (10kyr) security of CO2 storage at a basin scale. Simulations show that CO2 storage in regions with moderate abandoned well densities and that are regulated using current best practice will retain 96% of the injected CO2 over 10,000 years in more than half of cases, with maximum leakage of 9.6% in fewer than 5% of cases. Poorly unregulated storage is less secure, but over 10,000 years, less than 27% of injected CO2 leaks in half of the simulations; up to 34% leaks in just 5% of cases. This leakage is primarily through undetected and poorly abandoned legacy wells, and could be reduced through effective leak identification and prompt remediation of leakage. Natural subsurface immobilisation means that this leakage will not continue indefinitely. Regulators can most effectively improve CO2 storage security by identifying and monitoring abandoned wells, and perform reactive remediation should they leak. Geological storage of CO2 is a secure, resilient and feasible option for climate mitigation even in overly pessimistic poorly regulated storage scenarios and thus CO2 storage can effectively contribute to meeting the Paris 2015 target.

Identification of Multi-Dimensional Electromagnetic Information Leakage Using CNN

The unintentional electromagnetic (EM) radiation from electronic information equipment will cause the leakage of information. EM information leakage identification is an important research area of EM information security. Previous studies only focused on a particular type of information leakage, ignoring that there are multiple types of information contained in the leaked EM signal. This paper presents a multi-dimensional information leakage model for the first time, which reveals the multiple information leakage risks in an unintentional EM radiation. The EM information leakage of computer monitors is taken as an example to verify the model. Furthermore, a Convolutional Neural Network (CNN) based multi-dimensional information identification method is proposed. This novel method designed a unified neural network architecture to identify multiple information leaked by the same EM radiation from the monitor. It overcomes the limitation of available identification methods that need to definite leakage characteristics for each type of information leakage. The effectiveness of the method is validated by contrast experiments.

Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine

A pipeline's safe usage is of critical concern. In our previous work, a fiber Bragg grating hoop strain sensor was developed to measure the hoop strain variation in a pressurized pipeline. In this paper, a support vector machine (SVM) learning method is applied to identify pipeline leakage accidents from different hoop strain signals and then further locate the leakage points along a pipeline. For leakage identification, time domain features and wavelet packet vectors are extracted as the input features for the SVM model. For leakage localization, a series of terminal hoop strain variations are extracted as the input variables for a support vector regression (SVR) analysis to locate the leakage point. The parameters of the SVM/SVR kernel function are optimized by means of a particle swarm optimization (PSO) algorithm to obtain the highest identification and localization accuracy. The results show that when the RBF kernel with optimized C and γ values is applied, the classification accuracy for leakage identification reaches 97.5% (117/120). The mean square error value for leakage localization can reach as low as 0.002 when the appropriate parameter combination is chosen for a noise-free situation. The anti-noise capability of the optimized SVR model for leakage localization is evaluated by superimposing Gaussian white noise at different levels. The simulation study shows that the average localization error is still acceptable (≈500 m) with 5% noise. The results demonstrate the feasibility and robustness of the PSO–SVM approach for pipeline leakage identification and localization.

Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy.

Optimal joint deployment of flow and pressure sensors for leak identification in water distribution networks

ABSTRACTA multi-objective optimization methodology is proposed herein for accurate identification of leakage in water distribution networks (WDNs) using pressure and flow sensors. We first model leakage at potential nodes using the EPANET software, and then divide WDN into near-homogenous zones using k-means clustering algorithm based on geographic distribution of nodes. Finally, flow and pressure sensors locations are optimized using the NSGA-II algorithm to identify the leakage zone accurately. Novelty of the proposed approach lies in sequential optimization of flow and pressure sensors placement, which helps improve the accuracy of leakage zone identification in WDNs. The objective functions of this study are: 1) maximizing accuracy of identified leakage zone and 2) minimizing number of sensors (and hence operational costs). Simulation results of the Mesopolis WDN corroborate the efficiency and effectiveness of the proposed approach.

Identification of multiple leaks in pipeline II: Iterative beamforming and leak number estimation

Abstract This paper considers the problem of detecting multiple leaks in a water-filled pipe using transient waves. A previous paper by the authors proposes a maximum likelihood (ML) method for leak detection based on an approximate linear model of wave propagation in pipe and shows that it is efficient, robust with noise, and super-resolved. However, that ML method needs to solve an N-parameter optimization problem (N is the leak number) which is high dimensional, non-convex and has many local maxima when N is large. The present paper deals with the multiple-leak detection problem where the leak number is high. A ML based iterative procedure, known as the iterative beamforming (IB) method, is proposed. Given an initial value of model parameter (locations and sizes of leaks) in each iteration, the complex inverse problem with 2 N unknown parameters is decomposed into N one-dimensional optimization problems, which largely decreases the computational complexity. More specifically, IB iteratively estimates the contributions from various leaks to the pressure head measurement and then performs a 1-D ML method on these estimates. In order to estimate the unknown number of leaks, model selection methods, known as Akaike information criterion (AIC) and Bayesian information criterion (BIC), are used and they are proven to be efficient to estimate the actual leak number. Numerical simulations show that five leaks can be accurately identified with super-resolution and the leak number can be correctly estimated using the methods proposed in this paper.

Indocyanine Green Lymphangiography as an Adjunct for the Optimal Identification and Management of Lymphatic Leaks in the Groin.

The treatment of lymphatic leaks and lymphoceles in the groin can be challenging with no optimal management determined to date. We postulate that indocyanine green (ICG) lymphangiography improves visualization of the site of a lymphatic leak and can optimize their management. A retrospective review was conducted of all cases in which ICG lymphangiography was used in the management of lymphatic leaks in the groin over an 18-month span. The inciting surgical procedure resulting in the leak was determined. Following thorough debridement, ICG was injected intradermally in the distal extremity and the site of the lymphatic leak was documented (superficial or deep) and oversewn. Outcomes were reported with regard to healing, infection, time to drain removal, and adjunctive procedures. Fifteen patients underwent ICG lymphangiography during the surgical treatment of a lymphatic leak in the groin during the study period. In all cases, the site of the lymphatic leak was accurately identified and oversewn. In eight cases, the site of the lymphatic leak was in the subcutaneous tissue superficial to the femoral vessels rather than medial to the femoral vessels in the area of the lymph node basin. A local muscle flap was used in 10 cases simultaneously. All wounds healed primarily without an associated wound or infection. ICG lymphangiography facilitated the identification of lymphatic leaks in the groin and optimized their management in these challenging cases, many of which may have been missed if the area around the inguinal lymph node basin was treated exclusively.

Performance of a novel real-time respirator seal integrity monitor on firefighters: Simulated workplace pilot study

Millions of workers, including firefighters, use respiratory protective device. The key aspect in assuring the intended protection level of a respirator is its fit. However, even if the respirator originally fits well, the faceseal may be breached during its use. Until now, there have been no practically viable, inexpensive means to monitor the performance of a respirator during actual use. A novel Respirator Seal Integrity Monitor (ReSIM) was developed and recently evaluated on manikins by our team. The objective of this study was to evaluate the ReSIM effectiveness on respirator-wearing firefighters exposed to aerosols while performing simulated routine operational activities. Initially, 15 subjects were recruited for the study. Following a preliminary investigation that resulted in modifications in the ReSIM prototype and testing protocol, a subset of nine firefighters was chosen for a full-scale evaluation. The testing was conducted in a 24.3-m3 exposure chamber using NaCl as the challenge aerosol. Controlled faceseal leaks were established by opening a solenoid valve for 10, 15, or 20 sec. Leaks were also established as the tested firefighter slightly repositioned the respirator on his/her face. During the testing, the ReSIM measured particles inside a full-face elastomeric respirator with a 72.7% leak detection sensitivity (probability of correct leak identification) and an 84.2% specificity (probability of correct identification of the intervals which are absent of any leak). After adjusting for false negatives and persistent false positives, sensitivity and specificity increased to 83.6% and 92.2%, respectively. The factors causing minor limitations in leak detection sensitivity and specificity can be attributed to variability among subjects, moisture’s effect on the particle sensor, and some in-mask sampling bias. In conclusion, the ReSIM can promptly detect the breach in a respirator faceseal with high sensitivity and specificity. Due to its capability to alert the wearer of possible overexposure to hazardous aerosols, the ReSIM concept has a remarkable potential to be applied in various working environments, where respirators are used.

Above zone pressure interpretation for leaky well characterization and its identification from leaky caprock/fault

Pressure transient analysis has long been used for reservoir characterization. Above-zone (AZ) pressure has been recently investigated for inferring leakage pathway characteristics in leakage events from subsurface injection operations. The recorded pressure in the AZ should be purely related to leakage and therefore it can be safely inverted to deduce leakage characteristics. It is crucial to evaluate fluid leakage through abandoned wells to plan for further measurements of leakage prevention. However, the recorded AZ leakage signal may not be related to leaky well(s). Therefore, identification and spatial investigation of well leakage is required for leakage evaluation. In this paper, we propose a pressure interpretation method for early detection of leaky pathways, applying two observation points in the AZ. We distinguish leaky well, fault and caprock based on their corresponding flow regime identification. We show that the pressure difference of the two observation wells can be applied as a proxy for unknown leakage rate, which is crucial for leakage identification as well as characterization. Results show that the estimated location of the leaky well, leakage coefficient, and the leakage rate are in good agreement with the actual values. The estimated leakage coefficient of the leaky well can be used to evaluate well leakage in multiphase systems such as carbon dioxide leakage in deep saline aquifers.

Compressive Sensing-Based Multiple-Leak Identification for Smart Water Supply Systems

In this paper, the identification of multiple leaks in pipes based on transient waves is studied, which is, however, quite challenging due to its nonconvex nature with lots of local optima. Existing approaches need the number of leaks and suffer from a huge computational complexity that is increased exponentially with the number of leaks. To provide a scalable solution, we propose a compressive sensing (CS) framework to solve the multileaks identification. We first exploit the sparseness nature of leak locations through spatial sampling. Then, we formulate the multileak identification as a CS problem, where the spatial sample-dependent components form a basis matrix and the leak sizes are viewed as a sparse signal. We establish the convergence of spatial sampling mismatch and the two-restricted isometry property of basis matrix to justify the proposed CS framework. The proposed CS framework renders a superior-performance solution to multileak identification and its computational complexity is linear with the number of leaks, which is a significant technical improvement over existing approaches. In addition, a closed-form Cramer-Rao lower bound (CRLB) on the leak localization errors is derived. A geometric insight of CRLB evolution is presented to give us an intuitive understanding of the contribution of new measurements to leak localization performance.

Discrepancies in Radiograph Interpretation Between Pediatric Radiologists and Pediatric Intensivists in the Pediatric or Neonatal Intensive Care Unit

BackgroundIn pediatric intensive care units (PICUs) and neonatal intensive care units (NICUs), patient management decisions are sometimes based on preliminary interpretations of radiographs by pediatric intensivists (PIs) before a formal interpretation by a pediatric radiologist (PR). ObjectiveTo quantify and classify discrepancies in radiographic interpretation between PRs and PIs in the PICU and NICU. Materials and MethodsThis institutional review board–approved multi-institutional prospective study included three PRs and PIs at two PICUs and three NICUs. Interpretations of chest and abdominal radiographs by PIs and PRs were recorded on online forms and compared. Discrepancies in interpretations were classified as “miss,” “misinterpretation,” or “overcall.” The discrepancies were also categorized as “actionable” or “nonactionable” based on extrapolation of the ACR actionable reporting work group’s list of actionable findings. ResultsIn 960 radiographic interpretations, the total, nonactionable, and actionable discrepancy rates between PRs and PIs were 34.7%, 26.8%, and 7.9%, respectively. The most common actionable discrepancies were line or tube positions and identification and interpretation of parenchymal opacities in the lungs. Identification of air leaks in the PICU and differentiation of normal from abnormal bowel gas patterns in the NICU followed in frequency. Air leaks accounted for 1% of total discrepancies and 11% of actionable discrepancies. Most discrepancies were nonactionable and included retrocardiac atelectasis and mischaracterization of neonatal lung disease in the PICU and NICU, respectively. ConclusionAlthough the total discrepancy rate was high, most discrepancies were nonactionable. Actionable discrepancies were predominantly due to line and tube position, which should be an area of focused education.

Identification of multiple leaks in pipeline: Linearized model, maximum likelihood, and super-resolution localization

Abstract This paper considers the problem of identifying multiple leaks in a water-filled pipeline based on inverse transient wave theory. The analytical solution to this problem involves nonlinear interaction terms between the various leaks. This paper shows analytically and numerically that these nonlinear terms are of the order of the leak sizes to the power two and; thus, negligible. As a result of this simplification, a maximum likelihood (ML) scheme that identifies leak locations and leak sizes separately is formulated and tested. It is found that the ML estimation scheme is highly efficient and robust with respect to noise. In addition, the ML method is a super-resolution leak localization scheme because its resolvable leak distance (approximately 0.15 λ min , where λ min is the minimum wavelength) is below the Nyquist-Shannon sampling theorem limit ( 0.5 λ min ). Moreover, the Cramer-Rao lower bound (CRLB) is derived and used to show the efficiency of the ML scheme estimates. The variance of the ML estimator approximates the CRLB proving that the ML scheme belongs to class of best unbiased estimator of leak localization methods.

Prediction and Early Identification of Anastomotic Leaks after Colorectal Surgery

Anastomotic leaks (AL) still represent a major possibly life-threatening complication after colorectal surgery. The clinical presentation varies from mild symptoms to peritonitis and sepsis, which toughens the early diagnosis even for experienced surgeons. Numerous risk factors have been identified in the development of AL. The presence of bacterial strains such as Pseudomonas aeruginosa or Enterococcus faecalis are associated with higher AL rates, thus antibiotic prophylaxis seems to reduce complications. Male gender, advanced age, higher ASA fitness score, malnutrition and personal history of radiotherapy, diabetes mellitus and chronic kidney disease also lead to higher AL rates. The localization of the tumor also represents an important risk factor, as distal tumors have been identified as a predictor for AL. No differences have been found between open versus laparoscopic surgery as well as hand-sewn versus stapled anastomoses. The timing of the operation and the experience of the operating teams also affects both short and long-term. Early identification of AL is the key to reduction of mortality rates, thus scoring systems such as the Dutch Leakage Score have been developed and validated to aid surgeons for timely diagnosis. Modern imaging techniques and laboratory biomarkers further shorten the delay to a proper and early diagnosis. Computed tomography can identify even subclinical AL, leading to improved outcomes. Laboratory biomarkers such as C-reactive protein and procalcitonin are validated by large randomized studies as useful tools for exclusion of AL, possessing high negative predictive values.

Construction and tests of the research stand with built-in correctors for the detection of gas outflow from long pipelines

The localization and identification of small gas leaks from a damaged gas pipeline is a very important but at the same time problematic and difficult process to carry out. Quick identification, estimation and precise localization of the leak leads to minimizing the financial and equipment losses resulting from damage. The most commonly used continuous internal methods of leak identification are based on pressure and mass intensity measurements flow at various places in the pipeline. The article presents a description of the construction of a test stand characterized by the correctors built in it and the examples of the signals obtained from its tests for different values of simulated leaks. The results obtained will be used to develop a new method for identifying and locating leakages from long pipelines. This method is based on the cross power spectral density of measured pipeline standard signals (pressure and mass flow) and the membrane displacement signals delivered from the test equipment (correctors) connected to the system.

Leaks during multiple-breath washout: characterisation and influence on outcomes.

Nitrogen multiple-breath washout (N2MBW) is increasingly used in patients with cystic fibrosis. The current European Respiratory Society/American Thoracic Society consensus statement for MBW recommends the rejection of measurements with leaks. However, it is unclear whether this is necessary for all types of leaks. Here, our aim was to 1) model and 2) apply air leaks, and 3) to assess their influence on the primary MBW outcomes of lung clearance index and functional residual capacity.We investigated the influence of air leaks at various locations (pre-, intra- and post-capillary), sizes, durations and stages of the washout. Modelled leaks were applied to existing N2MBW data from 10 children by modifying breath tables. In addition, leaks were applied to the equipment during N2MBW measurements performed by one healthy adolescent.All modelled and applied leaks resulted in statistically significant but heterogeneous effects on lung clearance index and functional residual capacity. In all types of continuous inspiratory leaks exceeding a certain size, the end of the washout was not reached. For practical application, we illustrated six different “red flags”, i.e. signs that enable easy identification of leaks during measurements.Air leaks during measurement significantly influence N2MBW outcomes. The influence of leaks on MBW outcomes is dependent on the location, relation to breath cycle, duration, stage of washout and size of the leak. We identified a range of signs to help distinguish leaks from physiological noise.

A new method for D2O to H2O leak detection and identification of leaky heat exchanger in PHWR by Cerenkov photon counting technique

In pressurized heavy water reactors, leaks from D2O primary coolant and moderator to H2O secondary coolant and other light-water systems in heat exchangers cannot be completely ruled out. High cost of D2O demands that its loss should be prevented to maximum extent possible. Traditionally D2O leak detection and identification of leaky heat exchanger is carried out by measurement of tritium activity in H2O. Since tritium emits low energy beta radiation, its concentration in H2O is measured by mixing it with liquid scintillation solution in a definite proportion in counting vial and counted in a Liquid Scintillation Analyzer. It is very sensitive method for leak detection, but identification of leaky heat exchanger is time consuming and may require low power operation or reactor shut down. In the new method, high energy beta emitting fission products, which emit Cherenkov photons in H2O, were used as the tracer. H2O was poured in 20 mL plastic vials without scintillator and counted on Liquid Scintillation Analyzer. D2O leak was identified by comparing the Cherenkov photon count rate with that of the blank. A discrimination ratio significantly higher than average Cherenkov photon count rate for all heat exchangers was used to identify the leaky one. The technique has advantageous over existing method of D2O leak detection, such as, (1) scintillation chemicals are not required (2) low power operation or reactor shut down is not required for identifying the leaky heat exchanger (3) no generation of radioactive chemical waste (4) on-power leak identification reduces generation of radioactive liquid waste.