Leak Size Research Articles

Two-Leak Case Diagnosis Based on Static Flow Model for Liquid Transmission Pipelines.

The article deals with a diagnosis of multiple leaks from liquid transmission pipelines using analytical methods. Such solutions, based on advanced mathematical models of pipeline flow dynamics, usually turn out to be very complex and time-consuming. However, under certain operating conditions, a simpler approach may also be useful. Such an idea is presented in this paper, proposing two simplified methods for diagnosing double leakages. In principle, these methods apply to both simultaneous and non-simultaneous leaks. The first one uses a static model of a pipeline involving two leaks and takes advantage of the minimization of the objective function defined as the squared deviation of the modeled pressures from the pressures measured on the pipeline. The second method uses a pipeline flow model of a static type in combination with a gradient indicator aimed at detecting leaks and employing algorithms assigned to determining the location and size of leaks. The results of methods' validation, based on tests carried out with the use of measurement data obtained from an experimental water pipeline, were also presented. The outcomes of the performed tests proved the methods' effectiveness in terms of detection, isolation, localization, and intensity estimation of both simultaneous and non-simultaneous double leakages.

Comparative Study of Leak Detection in PVC Water Pipes Using Ceramic, Polymer, and Surface Acoustic Wave Sensors.

The detection and location of pipeline leakage can be deduced from the time arrival leak signals measured by acoustic sensors placed at the pipe. Ongoing research in this field is primarily focused on refining techniques for accurately estimating the time delays. This enhancement predominantly revolves around the application of advanced signal processing methods. Additionally, researchers are actively immersed in the utilization of machine learning approaches on vibro-acoustic data files, to determine the presence or absence of leaks. Less attention has been given to evaluating the sensitivity, performance, and overall effectiveness of these sensors in leak detection; although acoustic methods have been successfully used for leak detection in metallic pipes, they are less effective in plastic pipes due to the high attenuation of leak noise signals. The primary thrust of this research centers on identifying sensors that not only possess sensitivity but also exhibit high efficiency. To accomplish this goal, we conducted an exhaustive evaluation of the performance of three distinct categories of acoustic sensors employed for detecting water leaks in plastic pipes: specifically, lead zirconate titanate (PZT) sensors, polyvinylidene fluoride (PVDF) sensors, and surface acoustic wave (SAW) sensors. Our evaluation encompassed the performance of PVDF and SAW sensors in leak detection, comparing them to PZT sensors under a variety of conditions, including different leak sizes, flow rates, and distances from the leak. The results showed that all three sensors, when they were placed in the same position, were able to detect water leaks in plastic pipes with different sensitivities. For small leaks (1 mm, 2 mm), the PVDF sensor showed the greatest sensitivity (0.4 dB/L/h, 0.33 dB/L/h), followed by the SAW sensor (0.16 dB/L/h, 0.14 dB/L/h), and finally the PZT (0.13 dB/L/h, 0.12 dB/L/h). Similarly, for larger leaks (4 mm, 10 mm), the PVDF sensor continued to show superior sensitivity (0.2 dB/L/h, 0.17 dB/L/h), followed by the SAW sensor (0.13 dB/L/h, 0.11), and finally the PZT sensor (0.12 dB/L/h, 0.1 dB/L/h), outperforming the PZT sensor. This suggests that SAW and PVDF sensors, have the potential to serve as valuable, cost-effective alternatives to traditional commercial leak noise transducers. The outcomes of this comparative study involving three acoustic sensors hold the potential to advance the development of robust and dependable systems for the detection of water leaks in plastic pipelines.

Study on the leakage and diffusion behavior of hydrogen-blended natural gas in utility tunnels

Incorporating the hydrogen-blended natural gas pipeline into utility tunnels increases the potential safety risk caused by gas leakage and diffusion. In this paper, a numerical model for the leakage and diffusion of hydrogen-blended natural gas in the tunnel was established to study the influences of ventilation mode, initial pipeline pressure, hydrogen blend ratio (HBR), and leak size on gas leakage and diffusion. The results indicate that gas accumulates at the tunnel's top under natural ventilation. When the HBR exceeds 20%, the safety risk in the tunnel increases significantly. Unlike natural ventilation, mechanical ventilation significantly reduces the high-concentration area's impact range. Under the dynamic adjustment of ventilation frequency, the leak size significantly impacts gas diffusion concentration, while the HBR has the slightest impact. When the HBR is no higher than 40%, the pipeline pressure is no higher than 0.4 MPa, and the leak size is no high than 5 mm, the accident ventilation frequency of 12 times/h could ensure the leaked gas concentration is lower than the alarm concentration. A leaked gas concentration prediction model was established to provide a basis for predicting the distribution of the leaked gas concentration in actual engineering.

MFCC-LSTM framework for leak detection and leak size identification in gas-liquid two-phase flow pipelines based on acoustic emission

Two-phase gas–liquid flows are crucial to the pipeline system. Due to their complicated flow state, existing leak detection techniques are unsuitable for two-phase flow pipelines. To avoid accidents caused by a leak of pipelines, we present a framework for combining the Mel-frequency cepstral coefficient and long short-term memory (MFCC-LSTM) based on acoustic emission (AE). A series of experiments are performed considering 1152 operating conditions, including flow pattern, leak size, direction, and location. In addition, the detection capability of the different features of AE signals combined with LSTM is discussed. The results show that the recognition accuracy of the MFCC-LSTM framework reaches 98.4%. Then, we further perform leak size identification under different flow patterns and found that the MFCC-LSTM framework still exhibits excellent performance. The proposed MFCC-LSTM framework provides a promising solution to identify the leak state and size in two-phase flow pipelines based on the AE technique.

Device-related thrombus after left atrial appendage occlusion in patients with atrial fibrillation: a prospective follow-up

Aim. To study the incidence, predictors, and clinical outcome of device-related thrombus (DRT) after left atrial appendage occlusion (LAAO) in patients with atrial fibrillation (AF).Methods. A prospective observational study included 120 patients with non-valvular AF who underwent LAAO with Watchman (n=92) and Amplatzer Amulet (n=28). The presence of device-related thrombus (DRT) was assessed at visits 45 days, 6 months, 1 year, 2 years, 3 years after implantation by transesophageal echocardiography.Results. A total of 11 (9.2%) patients had DRT during the follow-up period. The greatest number of thrombosis was observed after 45 days (n=4) and after 6 months (n=4). There was no significant difference in the incidence of DRT between device types. Independent predictors of thrombosis were: history of myocardial infarction (hazard ratio (HR) 12.88 [95% confidence interval (CI) 3.21-51.62]; p<0.001), chronic heart failure (HR 8.83 [95% CI 1.91-40.77]; p=0.005), residual leak size >5 mm in the early postoperative period (HR 6.13 [95% CI 2.53-14.86]; p<0.001) and the degree of spontaneous echo contrast during the initial examination (HR 9.09 [95% CI 1.36-60.58], p=0.023). There were no cases of thromboembolic complications associated with DRT. One patient developed a non-fatal stroke at 35 weeks of follow-up, while DRT was detected at the visit at the end of the 3rd year of follow-up.Conclusion. DRT after LAAO was observed in the early and long-term follow-up periods. This event was associated with the baseline patients’ characteristics and post-procedural aspects with no dependence on type of antithrombotic therapy.

Endoscopic vacuum therapy versus self-expandable metal stent for treatment of anastomotic leaks < 30mm following oncologic Ivor-Lewis esophagectomy: a matched case-control study.

Management of anastomotic leaks after Ivor-Lewis esophagectomy remains a challenge. Although intracavitary endoscopic vacuum therapy (EVT) has shown great efficacy for large dehiscences, the optimal management of smaller leaks has not been standardized. This study aims to compare EVT versus self-expandable metal stent (SEMS) in the treatment of leaks < 30mm in size, due to the lack of current data on this topic. Patients undergoing EVT (cases) or SEMS (controls) between May 2017 and July 2022 for anastomotic leaks < 3cm following oncologic Ivor-Lewis esophagectomy were enrolled. Controls were matched in a 1:1 ratio based on age (± 3years), BMI (± 3kg/m2) and leak size (± 4mm). Cases (n = 22) and controls (n = 22) showed no difference in baseline characteristics and leak size, as per matching at enrollment. No differences were detected between the two groups in terms of time from surgery to endoscopic treatment (p = 0.11) or total number of procedures per patient (p = 0.05). Remarkably, the two groups showed comparable results in terms of leaks resolution (90.9% vs. 72.7%, p = 0.11). The number of procedures per patient was not significant between the two cohorts (p = 0.05). The most frequent complication in the SEMS group was migration (15.3% of procedures). EVT and SEMS seem to have similar efficacy outcomes in the treatment of anastomotic defects < 30mm after Ivor-Lewis esophagectomy. However, larger studies are needed to corroborate these findings.

Research on pinhole accidental gas release in pipelines: Statistical modeling, real gas CFD simulation, and validation

The successful risk mitigation of Natural Gas (NG) leakage to reduce its environmental and economic impact depends chiefly on timely detection leaks and predicting the amount of gas release. In the present study, we investigated pinhole leaks and predicted the gas release rate for various leak to pipe diameter ratios and operating pressures ranging from 2 bar to 110 bar. We first set up a laboratory-scale experiment. The generalized likelihood ratio (GLR) is used as an advanced statistical hypothesis testing technique to detect any shifts in the mean and variance of the process measurements in real-time. These results improved the understanding of several leak detection systems and contributed to a reduction in false alarms in these systems. The presence of a leak was flagged almost immediately after it occurred, indicating the speed and efficiency of statistical techniques in detecting microleaks. The present computational fluid dynamics (CFD) study provides details on the entire leak flow field that are not possible to obtain with experimental methods. CFD is a critical tool in process safety management, helping to identify, analyze, and mitigate potential risks in natural gas pipeline. The CFD study proposes new correlations to predict the nominal leak volume flow rate by investigating the influence of leak size, pipe diameter, and pipe pressure for wide ranges of pressures. The correlations were derived from three-dimensional transient detachable eddy simulation model in a commercial CFD code (ANSYS Fluent R3). The correlations enhance the conventional models by incorporating the gas compressibility effect for high-pressure conditions. The percentage of error between the results of the CFD and delivered correlation fluctuated between 4% and − 5%, demonstrating the high accuracy of the new correlation.

Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells

Summary Underground gas storage (UGS) wells are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. The digital twin used for this application presents advantages compared with other solutions present on the market with regard to the simplified configuration; that is, with minimal input data, the system can produce an accurate and useful output, which is then used in the well integrity decision-making process. UGS wells present additional criticalities with respect to normal production wells due to their longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for the safe management of the well barriers. The proposed digital twin simulates the trends of pressure and temperature within each annulus and compares results with data from the field, allowing the identification of the position and size of leaks. A genetic algorithm is applied to optimize the placement of leaks on their specific barriers. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The studies presented show how the digital twin has been used on two wells with similar problems. At first, it has confirmed the necessity to put the well out of service as opposed to planning maintenance, thereby saving both time and cost. In the second case, it allowed the validation of a solution that led to a 60% reduction in failure consequence, allowing the well to continue operating without major costs or risks. The errors of the resulting simulations were always confined within the 0.5 bar limit highlighting its accuracy. The system has been in use for over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcases a clear readiness for predictive capabilities aimed to select, plan, and design fit for purpose mitigating actions.

Experimental and computational fluid dynamic (CFD) simulation of leak shapes and sizes for gas pipeline

In this paper, a parametric study has been carried out to predict the exit velocity of air through the leakage in the pipe with the help of CFD software ANSYS Fluent. The effect of air pressure in the pipe and the shape of leakage have been studied. Further experiments were also carried out to determine the exit velocity for the defined shape of leakage by varying the air pressure in the pipe. Experimentally, the velocity at a distance of 8 cm from the location of a leak in the horizontal plane was obtained with the help of differential pressure transducers. Using the experimental results, the computational results were validated. The results of the parametric simulation study showed that even for a pressure of 2 bars the velocity profile at the leak location indicates the supersonic state where the Mach number is greater than 1. The study is useful because it may be used as a foundation for risk assessment and safety management in the case of flammable gas leaks through gas pipes.

Linear Programming Models for Leak Detection and Localization in Water Distribution Networks

This study examined the benefits and limitations of formulating linear programming models for water distribution network leak detection and localization considering sequences of demands. First, the consistency of sensitivity matrixes for different demand magnitudes and spatially varying nodal demands was investigated to determine if different sensitivity matrixes are needed for each time step and to determine the demands to use for computing the gradient matrixes. Then several unconstrained and constrained models were developed and their detection and localization performance was evaluated for a network in Austin, Texas. To constrain the failure location in time and space, binary (zero–one) integer linear programming (BILP) was used. A set of threshold-based detection rules was applied to test for the anomalies in the time series, and they were compared for a range of realistic leak sizes. Based on the numerical results, the best optimization model was identified considering multiple detection metrics (detection effectiveness, efficiency, and localization). The best BILP model that constrains leak locations spatially and temporally outperformed the unconstrained model in detecting and locating relatively small leaks.

Micro-Leakage Image Recognition Method for Internal Detection in Small, Buried Gas Pipelines.

In order to resolve the problem that the sample of image for internal detection of DN100 buried gas pipeline microleakage is single and difficult to identify, a recognition method of microleakage image of the pipeline internal detection robot is proposed. First, nongenerative data augmentation is used to expand the microleakage images of gas pipelines. Secondly, a generative data augmentation network, Deep Convolutional Wasserstein Generative Adversarial Networks (DCWGANs), is designed to generate microleakage images with different features for detection in the pipeline of gas pipelines to achieve sample diversity of microleakage images of gas pipelines. Then, a bi-directional feature pyramid network (BiFPN) is introduced into You Only Look Once (YOLOv5) to retain more deep feature information by adding cross-scale connecting lines in the feature fusion structure; finally, a small target detection layer is constructed in YOLOv5 so that more shallow feature information can be retained to achieve small-scale leak point recognition. The experimental results show that the precision of this method for microleak identification is 95.04%, the recall rate is 94.86%, the mAP value is 96.31%, and the minimum size of identifiable leaks is 1 mm.

A Review of Leak Detection Systems for Natural Gas Pipelines and Facilities

Pipelines facilities, used for the transportation of natural gas in large quantities to homes and industries, remain the best economic, most reliable and safest mode of transport of energy. Despite these numerous advantages, gas pipelines have been enmeshed in various accidents and thefts, nonetheless this could be reduced if properly maintained and pipelines can last indefinitely without leaks. Pipelines are susceptible to leakages and rupture accidents as a result of age, corrosion, material defects, operational errors or other reasons. Pipeline failures may be caused intentionally (e.g. vandalism) or unintentionally (e.g. device/material failure and corrosion), which may result into irreversible damages such as financial losses, human casualties, ecological disaster and extreme environmental pollution. Leakages in natural gas facilities and installations require three vital aspects, namely: Gas Leakage Prevention, Gas Leakage Detection and Gas Leakage Mitigation. Many Gas Leak Detection methods are used for pipeline integrity management and especially for minimizing gas leakage. The performance of these methods depends on the approaches, operational conditions and pipeline networks. Also, there are some essential requirements and guidelines which must be met before we can consider any leak detection system suitable for production solutions, including sensitivity, reliability, accuracy and robustness. The attempt of this study is to carry out a critical review of these models, to ascertain the best model(s) applicable to natural gas leak detection. Keywords: Gas Leak Detection System, Leak Location, Leak Size DOI: 10.7176/JETP/13-2-02 Publication date: April 30 th 2023

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms.

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform.

1-D convolution neural network based leak detection, location and size estimation in smart water grid

ABSTRACT Water is one of the essential natural resources for survival, but the water transportation system faces significant challenges because of huge water loss due to leaky pipeline systems. An IoT based novel SWG prototype has been developed and reported in this work. The SWG comprises sensors and devices that can continuously and remotely monitor the pressure, temperature, flow, pH, turbidity, etc., of the water being transported. Moreover, a novel 1-D CNN model has been developed by creating an artificial leak on the pipeline that takes input data points as a chunk of 5-minute time series to the network and gives output in leak detection, location and size estimation simultaneously. Further, the developed model is compared with other state of the art machine learning techniques and the proposed model is found better in terms of accuracy which is 94.32%, 91.91% and 89.85% for leak detection, size estimation and location respectively.

Experimental Study of External Pipe Wall Erosion due to Water Pipe Leakage

Burst pipes can cause severe pressure drops and water pollution in water distribution networks. Small leaks can develop into pipe bursts over time due to interactions between pipes, water, and soil. This paper presents preliminary experimental results on the erosion of the outer wall of a pipe by solid particles when a leak occurs in a pressurized pipe and causes soil fluidization. A series of experiments were carried out with 50-mm-diameter and 3-mm-wall-thickness polyethylene (PE) pipes with various leak holes buried in silica sand bedding. The experiments investigated the effects of the leak size, shape, and direction on the erosion process. The findings indicated that the size of the leaks had the greatest impact on erosion rate, and erosion rate was nearly completely unaffected by the geometry of the leak hole or leak orientation.

Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network

This paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operating conditions of the pipeline. However, the multiple sources of acoustic emission hits, such as fluid pressure on the joints, interference noises, flange vibrations, and leaks in the pipeline, make the features less sensitive toward leak size identification in the pipeline. To address this issue, acoustic emission hit features are first extracted from the acoustic emission (AE) signal using a sliding window with an adaptive threshold. Since the distribution of the acoustic emission hit features changes according to the pipeline working conditions, a newly developed multiscale Mann–Whitney test (MMU-Test) is applied to the acoustic emission hit features to obtain the new vulnerability index feature, which shows the pipeline's susceptibility to leak and changes according to the pipeline working conditions. Finally, the vulnerability index is provided as input to a 1-D-CNN for leak detection and size identification, whose experimental results show a higher accuracy as compared to the reference state-of-the-art methods under variable fluid pressure conditions.

Combined Dual-Prediction Based Data Fusion and Enhanced Leak Detection and Isolation Method for WSN Pipeline Monitoring System

In a Wireless Sensor Networks (WSN) based fluid pipeline leak monitoring system, numerous sensors are deployed along the pipeline networks. A great amount of measurements are continuously transmitted from the sensor nodes to their corresponding sink nodes. The energy consumed on data transmission dominates the power depletion of a WSN system. To reduce the amount of data transmission and prolong the lifetime of WSN, in this paper, a Combined Dual-Prediction based Data Fusion (CDPDF) method is proposed. Transmissions are only triggered if the measurement is substantially different from the predicted value. Furthermore, unlike existing methods which establish the predictor by merely considering the measurements from a single sensor, the proposed CDPDF learns and updates the predictor by integrating measurements from multiple neighboring sensors, hence the spatial cross-correlation is taken into account and the prediction accuracy is significantly improved. In this paper, an Enhanced Leak Detection and Isolation (EnLDI) method is also proposed in which several important parameters, such as the friction factor and the pressure wave propagation speed, can be online updated, resulting in improvement of the leak localization accuracy. Experimental case studies are conducted. By employing the proposed CDPDF and EnLDI methods in pipeline networks monitoring, the accuracy of leak isolation is significantly increased with reduced data transmission demands. Note to Practitioners—This work delivers a hybrid scheme that combines machine learning based data fusion and transmission, with model-based leak detection and isolation. The work is motivated by the problem of high energy consumption on data transmission and poor leak diagnosis accuracy in WSN based pipeline networks monitoring system. To reduce the energy consumed during frequent transmissions among sensor nodes, in this paper, a machine learning based data fusion method is proposed which can eliminate most of the redundant transmissions. Among the investigated schemes, the Extreme Learning Machine (ELM) based predictor can not only achieve satisfactory prediction accuracy but also has low computational cost, hence it can be easily implemented in most of the embedded micro-controller systems in practice. At the base station of a WSN, in the leak diagnosis phase, traditional model-based methods employ the fixed model parameters which should be adjustable in different pressure and flow conditions etc. In this paper, an online model parameter estimation procedure is introduced and incorporated in the scheme designed to estimate the leak size and location, thus, the leak localization accuracy is significantly improved. Moreover, the algorithmic procedures, mathematical expressions, evaluation process and results are also provided for practical implementation.

Vibration-Based Discriminant Analysis for Pipeline Leaks Detection

Pipelines are useful for transporting liquids from one place to another. The main problem that often occurs in pipelines is leakage which results in production and financial losses. The importance of detecting pipeline leaks makes the industries look for effective detection methods to avoid bigger losses. Several previous studies have proven that the vibration-based method is successful in detecting leaks in pipelines. However, the vibration-based method used in the previous study is relatively complicated and requires specialists to interpret the results. This study proposes a machine learning-based detection method that can classify pipe conditions directly without the help of a specialist. The proposed method is vibration-based discriminant analysis; a machine learning algorithm that recognizes pipeline conditions from their vibration pattern instead of spectrum. The proposed method was tested on a test rig consisting of a closed-loop pipeline equipped with a leak-pipe test segment. The vibration signal is taken using an accelerometer placed on the leak-pipe test segment. Time domain vibration data is extracted using several statistical parameters which aims to reveal information related to pipe conditions. The vibration data collected is divided into two groups, namely training-data and testing-data. The discriminant analysis model is trained to recognize the vibration pattern of the pipeline using training-data and then tested using testing-data. There are four leak sizes introduced in this study, small, medium, and large. Meanwhile, normal condition (no leaks) is used as benchmarking. The study shows that the proposed method is effective in classifying four pipe conditions with the accuracy up to 95%.

Single-Use System Integrity IV: A Holistic Approach Based on Compiled Scientific Study Data.

This concluding article of the publication series provides an overarching summary of all study results presented in the three previous articles (1-3). Their interdependency in achieving a holistic approach to the integrity assurance of single-use systems (SUSs) employed in (bio)pharmaceutical manufacturing is finally illustrated. Two of those three studies were conducted to understand microbial ingress and liquid leak mechanisms in polymeric film material as determinants of the maximum allowable leakage limit (MALL) for SUSs using artificially created defects. The third study characterized gas flow through these defects-an essential variable for robust validation of physical integrity test methodologies based on gas flow. In all studies, the test samples used were 50 mm round patches of two ethylene vinyl acetate (EVA) multilayer films (300 μm and 360 µm thick) and a polyethylene (PE) multilayer film (400 μm thick). More than 1400 test samples with artificially created leaks were used in sizes ranging from 1 μm to 130 μm. The leaks were laser-drilled into the center of each patch. Microbial ingress and liquid leak testing under various process conditions resulted in a MALL of 2 µm for microbial integrity and the prevention of liquid leakages under most severe use-case conditions. The studies also demonstrated a close relationship between the occurrence of liquid leakage and microbial contamination. Different model solutions were used to evaluate the impact of liquid characteristics, mainly surface tension. The data were applied to build mathematical models for predicting the MALL under any intended use-case condition. By characterizing gas flow behavior over a broad pressure range using various defect sizes, it was possible to create formulas for three different flow regimes. These were suitable for calculating leak size in a defective product directly from the corresponding flow rate or vice versa. Finally, compilation of these different aspects enabled the authors to design and validate non-destructive physical integrity test methods having detection limits correlated to the MALL for SUSs.

Leakage aperture identification of natural gas pipeline based on compressed acquisition and DSAE

Traditional gas pipeline leak detection methods usually face challenges in that there are redundant data collected on account of the time-domain sampling theory and the valuable leakage information is hidden in the complex vibration signals. This paper put forward an intelligent aperture identification method for natural gas pipeline leakage. This method integrated the compression of monitoring data based on compressed sensing (CS) transformation and automatic feature extraction and identification based on deep neural networks, namely, denoising sparse autoencoder (DSAE). The compressed acquisition can greatly reduce the volume to be processed and effectively extract the aperture information. DSAE combined the merits of sparse auto-encoder (SAE), denoising coding and dropout to achieve robust feature extraction and aperture classification. Experimental results validate the effectiveness and good performance, and the proposed method can realize faster and more accurate identification of different leak sizes compared with the traditional methods.