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We use distributed fiber optic strain sensing to examine swelling of the fiber’s polymer coating. The distributed sensing technique that uses unmodified low-cost telecom fibers opens a new dimension of applications that include leak detection, monitoring of water quality, and waste systems. On a short-range length scale, the technology enables “lab-on-a-fiber” applications for food processing, medicine, and biosensing for instance. The chemical sensing is realized with unmodified low-cost telecom optical fibers, namely, by using swelling in the coating material of the fiber to detect specific chemicals. Although generic and able to work in various areas such as environmental monitoring, food analysis, agriculture or security, the proposed chemical sensors can be targeted for water quality monitoring, or medical diagnostics where they present the most groundbreaking nature. Moreover, the technique is without restrictions applicable to longer range installations.

One of the critical problems in wet-stock management is inaccurate (poor) tank calibration that masks the leakages from the underground storage tanks (USTs). Moreover, obtaining the correct tank parameters or re-calibration is an expensive procedure if not impossible. This study aims to prevent the masking effect of several tank parameters on the tank calibration chart and to improve the leak detection and wet-stock management for fuel storage tanks. This goal is achieved through obtaining the mathematical models for simple cylindrical tank that convert the measured liquid height to accurate liquid volume by taking into account the tank deformations. The accounted defor-mations in tank parameters are errors in radius, length, probe offset and axial and radial tank tilts, as well as volume change with temperature. The simulations using the actual data gathered from a commercial fuel service station showed that the approach developed in this study is valid. The deformation included tank models successfully pre-dicted the real fuel volumes. The results showed that the variance reduced from -300 L – +100 L range to -20 L – +20 L range for, which brings 67.2% improvement over the cumulative variance. This study also shows that obtaining the precise volume measurements by introducing models that account for the deformations in the UST is possible., Akaryakıt stok yönetiminde karşılaşılan en kritik problemlerden biri yer altı tanklarında (YAT) karşılaşılan hatalı tank kalibrasyonudur ve hatalı tank kalibrasyonu sızıntıları maskelemektedir. Üstelik, doğru tank parametrelerini elde etmek ya da yeniden tank kalibrasyon işlemi pahalıdır veya bazı durumlarda imkansızdır. Bu çalışmada amaçlanan, bu maskeleme etkisini ortadan kaldırmak ve yer altı akaryakıt depolama tankları için sızıntı tespitini ve stok yönetimini iyileştirmektir. Bu amaç doğrultusunda silindirik tank için tank deformasyonu hesaba katarak sıvı yüksekliğini doğru sıvı hacmine çeviren matematiksel modeller oluşturulmuştur. Göz önünde bulundurulan tank parametreleri yarıçap, uzunluk, bombe derinliği, ölçüm noktası, tank ekseni yönündeki (aksiyel) eğim ve tank eksenine dik yöndeki (radyal) eğim ve kesafettir (sıcaklıktan kaynaklanan hacim değişimi). Akaryakıt servis istasyonundan toplanan gerçek verilerin kullanıldığı benzetim çalışmaları, geliştirilen yaklaşımın uygulanabilir olduğunu göstermektedir. Yapısal deformasyonun dahil edildiği tank modelleri gerçek yakıt hacimlerini başarıyla tahmin etmiştir. Sonuçlar göstermektedir ki, tanklardaki stok deviasyonun (varyans) -300 L – +100 L aralığından -20 L – +20 aralığına düşmüştür. Bu sayede, kümülatif stok deviasyonunda %67.2 iyileştirme gerçekleştirilmiştir. Bu çalışma aynı zamanda göstermektedir ki, geliştirilmiş olan ve olası deformasyonları hesaba katan matematiksel modeller kullanılarak doğru hacmin yanı sıra, deforme olmuş tankın doğru parametrelerini tayin etmek de mümkündür.

Bu çalışma, boru hattı ağlarının sızıntı tespiti ve izolasyonu için verimli bir gözetleyici tasarımı sunmaktadır. Tasarlanan gözetleyici, dağıtık şekilde bulunan sızıntılara sahip çok katmanlı boru hattı ağlarına uygulanabilir. Birbirinden farklı sızıntıların konumu ve akış hızı verimli bir şekilde tahmin edilebilir. "Ayrıklaştırma Temelli Gözetleyici" olarak adlandırılan tasarlanan gözetleyici sistemin ayrıklaştırılmış modeline dayanmaktadır. Bilgisayar uygulamaları için, 150 metrelik bir boruda farklı konumlara ve akış hızlarına sahip iki sızıntı dikkate alınmıştır. Ölçüm ve kestirimlerden 5 saniye sonra doğru sızıntı tahminleri elde edilmiştir. Literatür gözetleyicileri ile karşılaştırıldığında, tasarlanan gözetleyici tarafından hızlı yakınsama ile eşzamanlı durum ve parametre tahminleri elde edilir., This paper introduces an efficient observer design for leak detection and isolation of pipeline networks. The designed observer can be applied to the multistage pipeline networks with distributed leaks. The position and flow rate of the several leaks can be estimated efficiently. The designed observer is based on the discretized model of the system so called "Discretization Based Observer". For computer applications, two leaks with different positions and flow rates have been considered in a 150 meter pipe. After 5 seconds measurements and estimations, accurate leak estimations have been obtained. Comparing to the literature observers, simultaneous state and parameter estimations are obtained with fast convergence by the designed observer.

Pipeline leak detection sensors are created by using fiber Bragg gratings (FBGs) that are inscribed through polyimide coatings of optical fiber with an infrared femtosecond laser and then specially packaged in materials susceptible to hydrocarbons. Depending on the sensor geometry, strain is either applied or released upon exposure to toluene or crude oil., Optical Fiber Sensors 2020, June 8–12, 2020 , Washington, DC, USA

The dataset presented in this article was collected in a laboratory flow circuit, which was designed to investigate high-viscosity flows. The data set is composed of 1200 s (equivalent to 12,000 samples) of mass flow and pressure measurements taken at five points along the pipeline. The first 300 s were recorded when the flow in the loop was composed only of glycerol. The remaining data were acquired when the flow was composed of a water–glycerol mixture. During the data acquisition, two extractions were produced. The research reported in [1] uses 160 s of the data provided here. This article explains in detail the experimental set-up and the principal instruments used for obtaining the dataset. The dataset is in the form of seven columns: Time, Mass Flow, Pressure 1, Pressure 2, Pressure 3, Pressure 4, Pressure 5, in supplementary Excel and Matlab files.

In this study, the performance of both surface and borehole time-lapse gravity monitoring to detect CO2 leakage from a carbon storage site is evaluated. Several hypothetical scenarios of CO2 migration in a leaky fault, and thief zones at different depths at the Kimberlina site (California, USA) constitute the basis of the approach. The CO2 displacement is simulated using the TOUGH2 simulator applied to a detailed geological model of the site. The gravity responses to these CO2 plumes are simulated using forward modeling with sensors at ground surface and in vertical boreholes. Results of inversion on one scenario are also presented. The surface-based gravity responses obtained for the different leakage scenarios demonstrate that leakage can be detected at the surface in all the scenarios but the time to detection is highly variable (10–40 years) and dependent on the detection threshold considered. Borehole measurements of the vertical component of gravity provide excellent constraints in depth when they are located in proximity of the density anomaly associated with the presence of CO2, thus discriminating multiple leaks in different thief zones. Joint inversion of surface and borehole data can bring valuable information of the occurrence of leakages and their importance by providing a reasonable estimate of mass of displaced fluids. This study demonstrates the importance of combining multiphase flow simulations with gravity modeling in order to define if and when gravity monitoring would be applicable at a given storage site.

Ageing infrastructure and declining water resources are major concerns with a growing global population. Controlling water loss has therefore become a priority for water utilities around the world. In order to improve their efficiencies, water utilities need to apply good practice in leak detection. To deal with losses in an effective manner, particularly from networks in water-scarce areas, water utility managers are increasingly turning to technology to reduce costs, increase efficiency and improve reliability. Companies that continuously invest in technology and innovation should see a positive return on investment in terms of improving daily operations and collection and analysis of network data for decisionmaking and forward planning. Methodologies for achieving the best results to reduce water losses are continuously evolving. Water utilities and equipment manufacturers are increasingly working together to stretch the boundaries of current knowledge. This is leading to some innovative technologies and new product development to complement current methodologies. This book reflects the situation at the time of publication. This second edition of the book updates practices and technologies that have been introduced or further developed in recent years in leakage detection. It outlines recent advancements in technology used, such as satellite aided methods in leak location, pipeline inspection with thermal diagnostics, inspection of pipelines by air using infrared or thermal imaging cameras, drones for leak detection activities and even sniffer dogs. In addition, it is enriched with new case studies that provide useful examples of practical applications of several leak detection practices and technologies. ISBN: 9781789060843 (paperback) ISBN: 9781789060850 (eBook) ISBN: 9781789060867 (ePub)

Early leak detection is of great importance for life-cycle maintenance and management of municipal pipeline system. Due to economic and technical efficiency, ductile iron pipe segments and socket joints are widely used in practice to construct water distribution systems. The ductile configuration of the socket joint allowing for large deformation constitutes the most common cause for water leakage. Using acoustic emission (AE) techniques, this paper presents an experimental study on leak detection of a water distribution system subject to failure of socket joint. The acoustic characteristics of leak signals in the socket and spigot pipe segments are investigated. After feature extraction and selection, a classifier based on artificial neural network (ANN) is established. It has been validated that the dominant frequencies of the AE leak signals due to the failure of the socket joint concentrate on 0–10 kHz. The proposed ANN-based method can achieve good estimation accuracy of 97.2% and 96.9% by using the feature set {Peak, Mean, Peak Frequency, Kurtosis} and {Mean, Peak Frequency}.

A novel data-driven leak detection and localization algorithm is proposed based on the Kantorovich distance concept. Mass flowrates and pressure measurements are used to identify possible change in the pipeline status. Based on the pipeline leak signature, leak is detected and the location is further inferred. The proposed method was applied successfully to a simulated pipeline in transient condition. The efficacy of the proposed method was also proven by applying it to an industrial pipeline network with controlled leak tests in real time. The method successfully detected both small (as small as 1% of the nominal flow rate) and large test leaks in the realistic pipeline. The time required to detect and localize leak with the proposed algorithm was much lower than the available commercial leak detection system. The accuracy of the proposed leak localization method was demonstrated to be better especially for the small leaks.

Summary Leak testing with air or tracer gas has made significant progress in the last decade with respect to technology, improved sensitivity and shorter cycle times. The motivation was driven by stricter environmental regulations and cost pressure. The article gives an overview of different leak testing methods. Furthermore a question guideline for the selection of the optimal leak detection method for different applications is introduced.

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

Galvanized steel pipes with screw thread connections are widely used in indoor gas transportation. In contrast with the failure of pipe tubes, leakage in this system is prone to occur in the screw thread connections. Aiming at this specific engineering application, a method based on acoustic emission (AE) and artificial neural networks (ANNs) is proposed to detect small gas leaks. Experiments are conducted on a specifically designed galvanized steel pipe system with the manipulated leak occurring in the screw thread connection to acquire the raw AE data. The features in the time and frequency domains are extracted and selected to establish an ANN model for leak detection. It has been validated that the developed ANN-based leak detector can achieve an identification accuracy of over 98%. It is also verified that the proposed model is effective even when the AE signals due to a small leak pass over two screw thread connections or an elbow connection.

A flow model is presented in this short communication to design a horizontal sampling method of soil-gas for pipeline leak detection. The soil-gas flow model is verified by air-extraction tests. A laboratory method is also designed in this short communication to evaluate the effective detection radius of organic volatile compounds (VOCs) using the horizontal sampling method. Results of laboratory tests demonstrate that the effective detection radius using the horizontal sampling method for pipeline leak detection is at least 30 m and 20 m for gasoline and diesel, respectively.

A district heating network (DHN) is one of the most important infrastructures in cities and towns of countries with colder weather. Citizens generally use DHN services for hot water supply all year round and also for space heating during winter. It is important that in case of an accident this service would be restored as soon as possible, causing minimal damage or inconveniences for the customers. In addition, it is important to minimise losses of the DHN operator. This study demonstrated a possibility of leak location in DHN using the data from the pressure sensors in the network, employing the negative pressure wave (NPW) method. The mass balance in the DHN is measured at the heating source, which acts as a confirmation of a leak in a closed system. The experiments in the real DHN were performed and the data was used to trace the leak location. Numerical modelling tools were used to model pressure transients during pipe break accident under various conditions in order to predict the effectivene...

Conventional water pipeline leak detection surveys employ labour-intensive acoustic techniques, which are usually expensive and not amenable for continuous monitoring of distribution systems. Many ...

Leak detection and localization in water pipeline networks is of paramount importance to industry, especially in regions where water is scarce. In this paper, we present a novel multi-modal and multi-scale approach for leak detection and localization in water pipeline networks, in which pressure measurements at various points on the network are used to localize the pipe segment in which the leak is occurring, and then the vibration sensors are used to localize the leak within this segment. In some situations where the complete pipeline model is not available, pressure data alone may not be effective in localizing the leak. However, in such a situation, by supplementing pressure data with vibration data, the leak can be localized, as these additional data are easier to acquire at arbitrary points, since vibration sensors are non-invasive. In order to validate the effectiveness of the approach that needs both pressure and vibration data, we simulate the pipeline model using EPANET that includes models for flow and pressure at various points on the pipeline, then integrate the vibration model with it in MATLAB, since EPNAET does not include models for vibration measurements. A case study of a pipeline network is considered, and the proposed scheme is used to detect and localize the leak. Extensive simulation results show the effectiveness of the proposed scheme in providing accurate leak detection and localization.

Thermal imaging captures information in the infrared spectrum of light invisible to people, thus it can provide valuable extra information. Innovative use of thermal imaging technology can therefore play an important role in many civil engineering applications. This paper provides insight into thermal imaging technology and its uses in civil engineering. A field example of early leak detection in an earth embankment using thermal imaging technology is presented. With ever-improving thermal imaging technology and decreasing cost of thermal imaging cameras, many future uses of thermal imaging in civil engineering are envisaged – from pipeline leak detection to structural integrity inspections, energy efficiency surveys and pollution monitoring. The small size of the equipment means it can also be carried by drones, offering access to remote or otherwise inaccessible areas.

Pipe leak location estimates are commonly conducted using Vibro-Acoustic Emission (VAE) based methods, usually using accelerometers or hydrophones. Successful estimation of a leak's location is dependent on a number of factors, including the speed of sound, resonance, backfill, reflections from other sources, leak shape and size. However, despite some investigation into some of the aforementioned factors, the influence of backfill type on a leak's VAE signal has still not been experimentally quantified. A limited number of studies have attempted to quantify the effects of backfill. However, all of these studies couple other variables which could be equally responsible for their observed changes in leak signal. There have been no controlled studies where one variable can be directly compared to one another (i.e. all variables remain constant, only changing backfill type). The aim of this paper is to better characterise the influence of backfill on a leak's VAE signal by individually isolating all variables. For the first time, this paper demonstrates the influence of backfill on leak VAE signal by keeping all other variables consistent. It was found that the backfill type had a strong influence on the frequency and amplitude of leak signals, which is likely to have a significant impact on the accuracy of leak location estimates.

Leaks have been detected and localized in water distribution in many countries for years, by using the cross-correlation of pipe vibration measured either side of a suspected leak and/or by using listening sticks. However, in modern plastic pipes this is problematic because of the strong coupling between the water and the pipe which results in significant attenuation of the coupled fluid-pipe wave, which in turn is responsible for the propagation of leak noise. Hence leak noise does not propagate as far as it did in older metallic pipes. This paper discusses how the vibro-acoustic behavior of the water-pipe-soil system influences leak noise propagation. To investigate this, a numerical model is developed, and the results are compared with those from an analytical model for two water pipe systems, one in the UK and one in Brazil. Experimental results are also presented. It is shown that the important difference between the two systems is the shear stiffness of the surrounding soil, which has a profound effect on both the speed of leak noise propagation and the attenuation of the noise along the pipe.

Concerns over mitigating methane leakage from the natural gas system have become ever more prominent in recent years. Recently, the U.S. Environmental Protection Agency proposed regulations requiring use of optical gas imaging (OGI) technologies to identify and repair leaks. In this work, we develop an open-source predictive model to accurately simulate the most common OGI technology, passive infrared (IR) imaging. The model accurately reproduces IR images of controlled methane release field experiments as well as reported minimum detection limits. We show that imaging distance is the most important parameter affecting IR detection effectiveness. In a simulated well-site, over 80% of emissions can be detected from an imaging distance of 10 m. Also, the presence of "superemitters" greatly enhance the effectiveness of IR leak detection. The minimum detectable limits of this technology can be used to selectively target "superemitters", thereby providing a method for approximate leak-rate quantification. In addition, model results show that imaging backdrop controls IR imaging effectiveness: land-based detection against sky or low-emissivity backgrounds have higher detection efficiency compared to aerial measurements. Finally, we show that minimum IR detection thresholds can be significantly lower for gas compositions that include a significant fraction nonmethane hydrocarbons.

The transient frequency response (TFR) method has been widely developed and applied in the literature to identify and detect potential defects such as leakage and blockage in water supply pipe systems. This type of method was found to be efficient, economic and non-intrusive for pipeline condition assessment and diagnosis, but its applications so far are mainly limited to single and simple pipeline systems. This paper aims to extend the TFR-based leak detection method to relatively more complex pipeline connection situations. The branched and looped pipe junctions are firstly investigated for their influences to the system TFR, so that their effects can be characterized and separated from the effect of other components and potential leakage defects in the system. The leak-induced patterns of transient responses are derived analytically using the transfer matrix method for systems with different pipe junctions, which thereafter are used for the analysis of pipe leakage conditions in the system. The developed method is validated through different numerical experiments in this study. Based on the analytical analysis and numerical results, the applicability and accuracy as well as the limitations of the developed TFR-based leak detection method are discussed for practical applications in the paper.

Lilongwe Water Board (LWB) is currently unable to meet Lilongwe City's water demand as evidenced by low supply coverage (65%) and intermittent water supply in the city. One of the major challenges is high levels of unaccounted for water (UFW) reported at 37% (2012), higher than the recommended 23% for developing countries. This study, done in Lilongwe City (Areas 15, 18 and 28), investigated water losses and partitioned UFW into real and apparent losses. Data collection involved data logging for pressures and flows at selected points in the network, meter testing, and water audits. This study estimated an average UFW of 37.5% for Lilongwe City and 33%, 44% and 20%, respectively, in the specific study areas (Areas 15, 18 and 28). The UFW in Lilongwe City was higher than recommended and was also higher than recommended in Areas 15 and 18 but within the acceptable limit for Area 28. High UFW levels in Areas 15 and 18 were mainly driven by real losses. The LWB should consider partitioning of its UFW to establish the main drivers, implement active leak detection programme and active pressure management in areas with high pressures.

Exacerbating the imbalance between demand for freshwater and available water resources is the sub-optimal performance of water distribution systems, which are plagued with leaks that cause significant losses of treated freshwater. This paper presents an approach for leak detection that involves continuous monitoring of the changes in the correlation between surface acceleration measured at discrete locations along the pipeline length. A metric called leak detection index is formulated based on cross-spectral density of measured pipe surface accelerations for detecting the onset and assessing the severity of leaks. The proposed non-invasive approach requires minimal human intervention and works under normal operating conditions of the pipeline system without causing any operational disturbances. The approach is demonstrated on a 76 mm diameter polyvinyl chloride pipeline test system considering varying leak severities. The preliminary results presented in this paper seem promising and lead to sever...

The disaster that occurred in communities one of which was a house fire caused by leakage of LPG which preceded the occupants leave the house. In anticipation of these events, LPG leak detection system remotely using SMS on a mobile phone handy so that residents know the situation / condition of LPG when leaving the house. when sensors TGS 2610 detect that the LPG in case of leak, then the residents will receive SMS 'Gas Bocor'. When in a safe condition, residents no longer receive SMS. When sending SMS residents 'cek' to the GSM module, the GSM module sending SMS 'aman' to residents. DOI: 10.24843/MITE.1501.08

Steigende Umweltbelastungen und verscharfte Umweltauflagen, zunehmende weltweite Verschlechterung der Sicherheitslage durch Sprengstoffanschlage, Drogentransport und -missbrauch, genauere Vorhersage von moglichen Naturkatastrophen wie z. B. Vulkanausbruchen, Bewertung von Umwelteinflussen durch Fracking und nach Unfallen in chemischen und petrochemischen Anlagen, polizeiliche Nutzung zur Aufklarung von Straftaten, lokale Lecksuche in Vakuumanlagen benotigen mobile und tragbare Analysesysteme fur in situ Messungen vor Ort. Der Artikel zeigt die sich daraus ergebenden Anforderungen an Vakuumsysteme, umrahmt von konkreten Anwendungsbeispielen. Vacuum systems for mobile applications Rising global pollution levels and therefore more stringent environmental regulations, the rising global danger regarding security in particular bombing attacks, drug trafficking and drug abuse, more precise prediction of natural desasters e.g. volcano eruptions and its effects to humans and air traffic, analysis of environmental effect from fracking, accidents in chemical and petrochemical plants, support police work in scouting offences, local leak detection on vacuum systems require portable and mobile analytical systems for in situ measurements. The article describes the requirements for the vacuum components and shows application examples for better understanding.

The widely used cross-correlation technique for leak detection requires that the leakage acoustic vibrations propagate along the gas pipelines over a significant distance as a single non-dispersive mode at a fixed velocity. In practice, the acoustic vibrations propagate along multiple paths including in-pipe gas and pipe wall, and the ones in different transmission paths possess different modal distributions and different dispersive behaviors, which bring a great challenge for leak detection in the gas pipelines. In this work, a modal extraction scheme is proposed by determination of dominant transmission path and vibration pick-up direction for improving leak detection in gas pipelines. Based on the vibration theory of cylindrical thin shell, the acoustical coupling between the in-pipe gas and the pipe wall is analyzed to determine a dominant transmission path. Then, the dispersive characteristics and modal distributions of acoustic vibrations at different directions in the dominant transmission path are analyzed using guided wave theory. Finally, the experimental investigations convince theoretical analyses and demonstrate that the axial vibrations in dominant transmission path are dominated by a single non-dispersive mode with high signal-to-noise ratio (SNR) in the frequency region 0–2.5 kHz. The findings provide useful leads for improving leak detection in the gas pipelines.

This paper deals with issues of detecting leaks in liquid transmission pipelines. It presents solutions of procedures (algorithms) applied to both negative pressure wave and gradient methods. These algorithms are aimed at achieving a satisfactory level of efficiency of a single leakage diagnosis. The methods are evaluated in terms of their implementation with a minimum number of measuring devices. In the case of the negative pressure wave method, apart from the standard pressure transmitters to measure the pipeline pressure, also the use of non-standard measuring devices, conventionally the so-called correctors, is considered. The signals generated by the correctors are characterized by good correspondence of the measured signal representing a change in the pressure to measurement noise level and the overall pressure transmitter range. All this, in combination with the developed algorithm, which apart from detecting a leak, is aimed at precise identification of a change in the signal related to the wave pressure front, i.e. the so-called inflection point, provides a high efficiency of leak detection. This has been proven by carrying out a number of experimental tests on a laboratory water pipeline. The tests involved simulations of both sudden as well slow leakages.

Thousands of industrial gas leaks occur every year, with many leading to injuries, deaths, equipment damage, and a disastrous environmental effect. There have been many attempts at solving this problem, but with limited success. This paper proposes a wireless gas leak detection and localization solution. With a monitoring network of 20 wireless devices covering $200\;\text{m}^2$ , 60 propane releases are performed. The detection and localization algorithms proposed here are applied to the collected concentration data, and the methodology is evaluated. A detection rate of 91% is achieved, with seven false alarms recorded over 3 days, and an average detection delay of 108 s. The localization results show an accuracy of 5 m. Recommendations for future explosive gas sensor design are then presented.

In water-supply pipeline leak detection and location, both the leak signals and blurred noises are closely related to the pipeline states and surroundings and most of the conventional noise-cancellation methods have to depend on the empirical parameters of either signals or noises. EMD (Empirical Mode Decomposition) is an adaptive signal decomposition method and is exclusive of base functions. A signal is decomposed into several IMFs (Intrinsic Mode Functions) in EMD, then the noise in a signal can be cancelled through removing uncorrelated IMFs. The existing EMD noise cancellation methods need to know the characteristics of either the wanted signal or the noise for rebuilding the noise-removed signal. However the characteristics of leak signals and noises are not fixed in various pipeline conditions, so the existing EMD noise cancellation methods can’t be directly applied in water-supply pipeline leak detection. This paper proposes an adaptive noise cancellation method based on EMD, in which the IMFs that don’t or less contain the components related to the leak can be removed through the cross-correlation between the IMFs and another signal collected at the either side of a suspect leak. In simulation analysis, the adaptive noise cancellation method can increase the SNRs (Signal to Noise Ratios) of leak signals as high as 16 dB. In processing practical pipeline vibro-acoustic signals, with the proposed method the peak of adaptive time delay estimate of leak signals, which determines the location of a leakage, becomes more distinguished, and thus the error of leakage location is improved.

Adopted technology allows engineers to test of propulsion plant (PP) on the stand with filling the fuel tank up to 2700 kg of hydrogen with implementation of special security measures and the use of systems emergency safety (SES), covering up to 60% of emergency situations. The paper considers the additional security measures ensuring the test for the rocket propulsion plant upper stages with increased doses of refueling (up to 7000 kg), based on the principle of preserving the hierarchical construction of test programs. A key factor to improve the efficiency of systems alert fire and explosion, SES and coefficient enrollment of emergency situations (0.8–0.9) is the research on systems of the earlier hydrogen leak detection and the use of inhibitors with nitrogen-deterrent to prevent an explosion of hydrogen and air (or oxygen) mixtures. The paper demonstrates the research results of the earlier hydrogen leak detection systems and implementation of plants for comparative testing for sensors for hydrogen dangerous concentration monitoring.

During the detection of pipeline leakages, false alarms of leak detection could be markedly reduced if the interference signals resulting from pressure regulating, pump regulating or valve movements could be accurately distinguished. A digital recognition method for interference signals and leakage signals based on a dual-sensor system is proposed in this paper. It is demonstrated that the direction of the signal can be recognized by a cross-correlation calculation between two signals from the dual-sensor, one of which undergoes forward linear interpolation and backward linear interpolation. Based on this theory, the interference signal and the leak signal can be discriminated exactly, and the distance between the two sensors in the dual-sensor system can be considerably reduced without needing to increase the sampling frequency. The monotonicity of the cross-correlation function is demonstrated, and a fast discrimination algorithm based on a binary extreme search method, which decreases the computational load and maintains global optimization, is also proposed. A pre-processing method of the actual signal is proposed to decrease the identity requirement for the two sensors in a dual-sensor system. In the experiment based on artificial signals, the proposed discrimination algorithm could achieve accurate recognition of the abnormal signal, and as such, the theory and application of pipeline leak detection based on dual-sensor systems are extended.

Failure of Leak Detection System (LDS) to detect pipeline leakages or ruptures may result in drastic consequences that could lead to excessive financial losses. To minimize the occurrence of such failure, the functionality of the LDS and the integrity of the pipeline should be assessed on a priority basis. This paper presents an integrated risk-based assessment scheme to predict the failure and the failure consequences of offshore crude oil pipelines. To estimate risk, two important quantities have to be determined, the joint probability of failure of the pipeline and its LDS and the consequences of failure. Consequences incorporate the financial losses associated with environmental damage, oil spill cleanup and lost production. The assessment provides an estimate of the risk in monetary value and determines whether the estimated risk exceeds a predefined target risk. Moreover, the critical year for the asset can be determined. In essence, the outcome of the assessment facilitates an informed decision-making about the future of the asset.

A phased array acoustic leak detection system for sodium heated steam generator has been proposed. The major advantage of the new system is it could provide information of acoustic source direction. An acoustic source of a sodium–water reaction is supposed to be localized while the background noise of the steam generator operation is uniformly distributed in the steam generator tube region. Therefore the new system could separate the target leak source from steam generator background noise. In the previous study, the methodology was proposed and basic performance was confirmed by numerical analysis. However, in the numerical analysis, acoustic transportation through the SG tube bundle was not modeled. In the present study, performance the proposed system has been confirmed in water experiments with mockup tube bundles.

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories. The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive. Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.

Early detection and precise location of leakage is of great importance for life-cycle maintenance and management of municipal pipeline system. In the past few years, acoustic emission (AE) techniques have demonstrated to be an excellent tool for on-line leakage detection. Regarding the multi-mode and frequency dispersion characteristics of AE signals propagating along a pipeline, the direct cross-correlation technique that assumes the constant AE propagation velocity does not perform well in practice for acoustic leak location. This paper presents an improved cross-correlation method based on wavelet transform, with due consideration of the frequency dispersion characteristics of AE wave and the contribution of different mode. Laboratory experiments conducted to simulate pipeline gas leakage and investigate the frequency spectrum signatures of AE leak signals. By comparing with the other methods for leak location identification, the feasibility and superiority of the proposed method are verified.

Intelligent leak detection is an essential component of a underground water supply pipeline network suchas a smart water grid system. In this network, numerous leak detection sensors are needed to cover all of thepipelines in a specific area installed at specific regular distances. It is also necessary to determine the existence ofany leaks and estimate its location within a short time after it occurs. In this study, the leak signal properties andfeasibility of leak location detection were investigated when concurrent leaks occurred at two points in a pipeline.The straight distance between the two leak sensors in the 100A sized cast-iron pipeline was 315.6 m, and theirsignals were measured with one leak and two concurrent leaks. Each leak location was described after analyzingthe frequency properties and cross-correlation of the measured signals. Keywords: Concurrent Leaks at Two Points, Cross-Correlation, Signal Analysis, Time Arrival Difference, WaterLeak Detection[Received: January 18, 2015, Revised: January 27, 2015, Accepted: February 3, 2015] 인천대학교임베디드시스템공학과, Department of Embedded Systems Engineering, Incheon National University, Incheon 406-772, Korea (E-mail:YSL@incheon.ac.kr)

While most of the available Leak Detection Systems (LDS) can detect pipeline leaks, leak localization is still an unresolved problem. The main reason for this problem is the limited number of sensors installed in long pipelines. Because of lack of measurements, precise leak location cannot be easily determined. This study uses particle filter as soft-sensor to estimate the states at intermediate locations with the available end-point measurements. The residuals between the estimated states and the real measurements at the intermediate locations are then used for leak detection and localization. The proposed method can improve the leak localization accuracy by proper use of the intermediate pressure measurements. Simulation results show the efficacy of the proposed method.

The use of Non-Dispersive InfraRed (NDIR) detection technology as a basis for a Hazardous Area Classification is described. Methods for identifying fugitive emission leaks using NDIR technology, quantifying leak data and applying the results to document the basis for a hazardous area classification are presented. Limitations associated with the technology are also defined. A case history of a gas and oil production facility is provided to illustrate how a hazardous area classification design may be implemented using the technology.

Made available in DSpace on 2018-12-11T17:12:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 Chinese Academy of Sciences Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) National Research Council Canada This paper is concerned with time delay estimation for the detection of leaks in buried plastic water pipes using the cross-correlation of leak noise signals. In some circumstances the bandwidth of over which the signal analysis can be conducted is severely restricted because of resonances in the pipe system, which manifest themselves as peaks in the modulus of the power spectral and cross spectral densities, and deviations from straight-line behaviour in the phase of the cross spectral density. The result can be a cross-correlation function in which it is difficult to estimate the time delay accurately. This paper describes a procedure in which the shape of the cross-correlation function can be significantly improved, resulting in an unambiguous and clear estimate of the time delay. The frequency response function(s) of the resonator(s) responsible for the resonance effects are first determined and then the data is processed using the model(s) of the resonators to remove these effects. This enables more signal processing to be conducted, potentially over a much wider bandwidth, further improving the shape of the cross-correlation function. The process is illustrated in this paper using hydrophone measured data at a leak detection facility. The current limitation in the process is that it is carried out manually, which could potentially restrict its application in practical acoustic correlators. The challenge now is to develop an algorithm to carry out the procedure automatically. Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences Department of Mechanical Eng. State University of São Paulo (UNESP), Ilha Solteira Campus, Av. Brasil Centro, 56 Beijing Municipal Institute of Labour Protection AECOM Infrastructure & Environment UK Limited Department of Biosystem Eng. State University of São Paulo (UNESP), Tupã Campus, Av. Rua Domingos da Costa Lopes, Jardim Itaipu, 780 Institute of Sound and Vibration Research University of Southampton Department of Mechanical Eng. State University of São Paulo (UNESP), Ilha Solteira Campus, Av. Brasil Centro, 56 Department of Biosystem Eng. State University of São Paulo (UNESP), Tupã Campus, Av. Rua Domingos da Costa Lopes, Jardim Itaipu, 780

We present first-time ever implementation of an innovative biomonitoring technology (High-Frequency Non-Invasive Valvometry) on an offshore oil producing platform located in the Arabian Gulf (TOTAL Abu Al Bukhoosh - UAE). This technology is unique; it is a tool allowing continuous in situ monitoring of water quality and detection of hydrocarbons at extremely low levels. This constitutes a key step in responsible management to minimize environmental footprint through the development of an advanced technology for operational use. This technology, developed by CNRS and University of Bordeaux (France), previously tested in a Research & Development program by TOTAL in laboratory, artificial outdoor river streams, and coastal sites, uses bivalves equipped with micro-electrodes recording their opening and closing activity. The latter is interpreted in terms of natural behavior and reaction to stressors. For offshore implementation, a Technical Committee with contributors from all technical departments was established, in order to identify and resolve all operational constraints and issues. The emblematic local pearl oyster Pinctada radiata was selected, and an agreement with the Environment Agency Abu Dhabi enabled appropriate local species collection and preparation. Effective leadership and strong involvement and collaboration of all technical departments in Abu Dhabi and France were key component in achieving success. All technical issues were assessed: HSE, technological safety, logistics, construction, IT, maintenance, metocean data…. Pearl oysters were collected and equipped with electromagnets at the Al Mirfa oyster farm with the support of the Environment Agency of Abu Dhabi. The installation solution adopted consists of valvometers (with multiparameter water probes to measure water temperature, oxygen content, and chlorophyll a), positioned along a suspension cable under the platform and securely moored at seafloor and platform structure. This solution avoids the use of divers (hence reducing both costs and safety risks), and allows easy equipment removal if needed. Crane and 1.3T deadweight were used to install the equipment and ensure mooring. Installation of two valvometers and two water probes was successfully completed in April 2017, before seawater reached a high temperature, to avoid mortality of oysters when manipulated. Valvometers and water probes are installed close to the seabed (-26m water depth) and in the water column (-10m water depth). The system is planned to run until March 2018. Preliminary results are shown and demonstrate that this technology is operationally viable for in-situ continuous water quality monitoring in a real industrial context such as an offshore oil producing platform. Natural biological rhythms of pearl oysters Pinctata radiata are well reconstructed from HFNI Valvometry data (circadian and circatidal cycles), while changes in water quality can be observed from valvometry bar codes data logs and animals' growth rate indices. Year-round data acquisition and capture will data to be generated to give a detailed characterization of oysters' behavior for biomonitoring purpose. As a result, a step change in environmental monitoring solutions for O&G industry is expected to come from this new technology and approach.

Leak in well bore tubulars & packers leads to communication between tubing/casing or Short/Long strings thus causing enormous production losses in Oil & Gas industry. Moreover, it can cause cross flow between different reservoirs. Sometimes leak may lead to charging of shallow formations thus posing a big threat to well integrity. The detection of the source of communication presents a big challenge to the industry. Knowing the precise location of a leak helps in designing an effective remedial action thus reducing downtime and repair costs. When a fluid leaks, it moves from the high-pressure side through the leak point to the low-pressure side, where it expands rapidly and produces a turbulent flow creating noise and temperature changes. Conventional noise and temperature tools are not capable of detecting the leak point with high confidence. However, the new generation Noise and Temperature logging tools have been able to locate such leaks precisely. This paper presents successful use of Spectral Noise Logging in combination with High Precision Temperature logging (HPT-SNL) in a dual-completion oil well in Kuwait Oil Company for identifying communication between the long and the short tubing strings. The acoustic tool contains a new-generation, high-sensitivity hydrophone – a piezo crystal element that records noise in a wide frequency range (0.1 – 60 kHz). Temperature tool utilizes a super-sensitive platinum sensor with a response time of less than one second. Leak was detected at the bottom blast joint of the long string that was causing water entry into the short string thus killing the well. The technology can be used in critical oil and gas wells to identify tubing and casing leaks and address well integrity issues. It can also be used in water injection wells to identify the zones of water intake.

A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA) and the symbiotic organism search (SOS) to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS) is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC) is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN), are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS). Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

The acoustic emission (AE) method is becoming popular for detecting leaks in municipal water mains where leaks are identified and the locations are determined through the interpretation of ...

International audience; Disasters involving gas leakage can cause serious damage to properties, to the environment and even endanger human lives. In these situations it is very important to locate the source of gas leakage as fast as possible. A swarm of robots is a solution specially efficient in missions involving exploration and search in unknown environments. This work proposes a strategy using behavioral rules and an idea inspired by the Particle Swarm Optimization algorithm to control a group of Unmanned Aerial Vehicles (UAVs) while searching for the source of a gas leakage. The solution was implemented in C++ using the ROS platform and was tested in simulations using the Pixhawk SITL simulator. Results show that after some time the UAVs converge to the source of leaking gas.