Leakage Conditions Research Articles

Non-invasive detection of hydraulic cylinder leakage using computer vision and time-frequency analysis

ABSTRACT This study presents a meticulous investigation facilitated by a bespoke high-pressure test rig. The rig stands as a cornerstone of the research endeavour, providing the capability to simulate diverse leakage scenarios under controlled conditions. With its capacity to replicate conditions akin to real-world hydraulic systems, including various levels of severity such as healthy operation, moderate leakage and severe leakage. The study delves into the intricate analysis of time-series discharge flow rate signals within this experimental framework. The study utilizes sophisticated signal processing techniques, particularly the Continuous Wavelet Transform (CWT), to observe frequency components and temporal occurrences. Despite prevalent challenges distinguishing between leakage severity levels, the CWT-based analysis provides crucial insights into the dominant low frequencies (2.3–3.3 hz) characterising all leakage conditions. An advanced computer vision-based methodology is devised to address the complexities inherent in leakage differentiation, integrating cutting-edge models such as You Only Look Once (YOLO-V7) and You Only Look Once-Neural Architecture Search (YOLO-NAS). These models are meticulously trained using annotated CWT images of flow rates corresponding to different leakage conditions. Notably, the superiority of YOLO-NAS in terms of both speed and accuracy underscores its efficacy in automated leakage detection. In summary, this comprehensive approach, underpinned by the innovative hydraulic test rig and advanced signal processing coupled with state-of-the-art computer vision techniques, presents a significant advancement in the realm of internal leakage detection in hydraulic systems.

Study on the impacts of refrigerant leakage on the performance and environmental benefits of heat pumps using R513A as replacement of R134a

The escalating threat of global warming has highlighted the imperative to address the greenhouse effect. R513A is recognized as a viable substitute for R134a, providing a lower global warming potential (GWP) while preserving similar thermodynamic properties. However, refrigerant leakage is one of the common faults in heat pump equipment. For industrial heat pumps, refrigerant leakage can make the system less stable and affect normal industrial production, while long-term leakage can also affect the carbon emissions of the industry. When substituting refrigerants, it is crucial to consider not only their distinct properties under typical operating conditions but also the stability and environmental impact of the alternative refrigerant in cases of leakage. This paper focuses on experimentally evaluating the impact of using R513A to replace R134a on the performance of refrigeration systems under the condition of rapid refrigerant leakage. Then the life cycle climate performance evaluation (LCCP) theory is used to assist experimental results in evaluating the carbon footprints of R513A and R134a systems at different annual leakage rates. The results show that R513A has better stability than R134a when responding to rapid refrigerant leakage. This paper determines the changes in annual electricity consumption and indirect emissions under several annual leakage rates and finds that the impact of leakage on indirect emissions is also not negligible. During the utilization phase of the equipment, when leakage was taken into account, the carbon emissions of the R134a system were higher.

Engineering geological investigation of Gololcha dam for evaluation of leakage and abutment slope stability, Eastern Ethiopia

The Ethiopian economy is primarily dependent on agriculture, making the construction of water harvesting facilities, such as dams, crucial for improving the productivity of this sector. The ongoing construction of the Gololcha dam on the Kurkura River located in Eastern Ethiopia aims to enhance irrigation schemes in the region. However, the dam site's complex geological and structural conditions pose challenges related to leakage and slope instability. Hence, this study focuses on addressing the abutment slope stability and leakage condition of this dam. This study employed kinematic analysis and the Limit Equilibrium Method (LEM) to assess slope stability. Additionally, engineering geological mapping, discontinuity surveys, seismic refraction tomography (SRT), and in-situ permeability testing were used to evaluate the leakage condition of the dam site. Notably, the permeability and SRT survey results identified potential leakage zones to the depth of 35, 30, and 35 m at the left, right, and central foundations of the dam, respectively. The kinematic method revealed one planar and two wedge modes of failure in the slope section covered by slightly weathered and fractured basalt rock at the right abutment. Further stability analysis of these two modes of failures via LEM analysis indicated slope instability under saturated conditions, emphasizing the role of pore water pressure. Furthermore, LEM modeling was directly utilized using the Slide 6.0 software to analyze the slope stability condition of the left abutment of the dam. This modeling also uncovered instability under saturated conditions. Based on the study findings, this study recommended curtain grouting to address potential leakage, as well as slope flattening and removing unstable rock wedges and loose material to stabilize unstable slope sections.

Experimental Study on Coal Oxidation and Temperature Rise Under Reciprocating Air Leakage

ABSTRACT Reciprocating air leakage often occurs in the goaf of coal mines because of changes in internal or external pressure. However, its impact on the spontaneous combustion of coal in the goaf is uncertain. Therefore, a comparative study of constant air leakage and reciprocating air leakage was carried out using a self-built system. The results indicate that reciprocating air leakage promotes coal oxidation and produces higher CO levels compared to constant air leakage. Further investigation was carried out to examine the effect of air leakage rate and reciprocating half-cycle (referred to as half-cycle hereafter) on coal oxidation and temperature rise. The results show that there is a linear relationship between resistance and air leakage rate in the coal oxidation heating space; however, the half-cycle length has little effect on resistance. The influence of a fixed air leakage rate on temperature and CO concentration does not always increase, with the maximum value achieved at 1.2 m3/h. Shorter half-cycles result in higher temperatures and CO concentrations. Gradually increasing air leakage rates and shortening half-cycles can promote coal oxidation and temperature rise and lead to higher CO production compared to previous scenarios. Additionally, the effect of using four different air leakage rates or half-cycle lengths is greater than that of using two. These research findings offer a theoretical foundation for preventing and controlling coal spontaneous combustion in goafs under complex air leakage conditions.

Numerical optimisation of microbially induced calcite precipitation (MICP) injection strategies for sealing the aquifer's leakage paths for CO2 geosequestration application

Carbon capture and storage (CCS) in deep geological aquifers has shown to be the most viable option for mitigating the greenhouse gas effect of carbon dioxide (CO2) at a large scale. However, the underground formations often possess discontinuities in the caprocks, leaking the stored CO2. Potential leakage paths, such as abandoned wells, have been growing due to excessively unplugged oil and gas exploration wells. The leakage of CO2 from these wells is a major concern, considering their negative impact on the environment and compromising CO2 storage efficiency. Recently, microbially induced calcite precipitation (MICP) technology has proven to be an effective and sustainable method for reducing the permeability of geomaterials. Nevertheless, the MICP process involves intricate interactions among bio-chemo-hydraulics (BCH) domains to comprehend the reactive transport of biochemicals. The complex nature of the MICP process poses difficulties in setting the biochemical injection durations for a particular target distance at the given injection rate. Given this, the present study developed a coupled numerical model and employed it as a workable tool for optimising MICP injections to plug the abandoned well connecting two deep geological aquifers. Following that, the study evaluated the leakage of CO2 using flow migration rates in the untreated and MICP-treated leaky aquifer. The study proposed a novel optimisation strategy for biochemical injections under near and far-field leakage conditions. The sensitivity of biochemical injection durations on the attached bacterial amount and permeability in the leak was also determined. The observations from the present study indicated a complete reduction in the CO2 migration rates from the abandoned well due to a reduced permeability after MICP, thereby indicating the efficacy of the proposed optimisation methodology. Further, a cost analysis of the MICP treatment indicated a rational application cost with the target distance compared to the detrimental effects of CO2 leakage.

Quantitative assessment of double-cone seal leakage in canned reactor coolant pump based on double-cone ring stress

The double-cone seal is a crucial component of the canned reactor coolant pump (RCP), serving as a vital barrier to prevent coolant leakage. Its sealing status directly impacts the operational safety of the RCP. Given the intricate operational conditions and rigorous sealing requirements of RCP, there is a dearth of effective detection methods for minor leakage of double-cone seal. To address this issue, a quantitative assessment method for the leakage of double-cone seal based on double-cone ring stress is proposed. In this paper, the functional relationship between the double-cone ring stress and the specific pressure of the gasket seal is first derived. Subsequently, a leakage rate assessment model based on the double-cone ring stress is proposed by combining with the leakage rate expression of the metal gasket connection structure. Finally, the feasibility of the proposed method for the leakage detection of double-cone seal is verified by finite element simulation and leakage simulation test. The results demonstrate that the proposed method is capable of detecting small leakage conditions with a leakage rate in the order of 1 × 10−4 cm3·s−1, which is sufficient for the micro-leakage detection of double-cone seal.

Investigation of scaling-down experiments for accidental CO2 leakage dispersion risks in constant-pressure transportation pipelines within the CCUS process

Within the framework of strategies to prevent global warming, the safety of CO2 transportation pipelines requires thorough hazard analysis. Accidental leakage during long-distance CO2 transport, influenced by the size of the leakage opening, typically occurs under constant pressure conditions in the CCUS process. This paper introduces an innovative scaled experimental apparatus with an elliptical orifice, designed to simulate leakage due to free rupture in pipelines. The goal is to understand the risk characteristics of constant-pressure leakage in transportation pipelines. The results indicate that during leakage, the pressure drop in the source CO2 pipeline is less than 0.3013 MPa/s, which can be considered constant pressure leakage. The Mach disk size dm has a linear relationship with the elliptical leakage orifice ab. The scaled experiments also show that temperatures can drop to as low as −78°C, indicating significant low-temperature risks. This scaling experiment provides a new approach for predicting and mitigating diffusion risks in long-distance pipeline transportation by examining the relationship between pipeline length and orifice diameter. The research data and results contribute to a deeper understanding of the safety aspects of long-distance transportation of CO2 under constant pressure leakage conditions, providing a basis for risk assessment of leakage accidents and environmental protection measures.

Leak detection in water distribution networks based on deep learning and kriging interpolation method

ABSTRACT The burgeoning growth of urban areas has escalated the necessity for efficient and precise leak detection in water distribution networks. Automatic detection methods based on deep learning are a state-of-the-art research topic. In this paper, a methodology that combines deep learning and data imaging is proposed. The framework employs pressure monitoring data and is anchored on the following three pillars: (1) the generation of a comprehensive dataset, encompassing one year of leak-free demand data derived from Fourier Series analysis and monitoring pressure under normal and leak conditions, (2) the transformation of pressure time series into images using kriging interpolation, (3) establishing convolution neural network (CNN) and evaluating its performance of abnormal identification. The effectiveness of the proposed methodology is assessed in different image sets under various leak conditions. The findings reveal that this method meets dependable and effective outputs for leak detection, with the deep learning model achieving a high true positive rate (TPR) of 98% and an area under the curve (AUC) of 94%. This study provides invaluable information for strategic action planning and the enhancement of water loss management protocols, especially in situations where water utilities and regulatory authorities grappling with limited budgets and diminishing revenues.

Research on Leak Detection of Low-Pressure Gas Pipelines in Buildings Based on Improved Variational Mode Decomposition and Robust Kalman Filtering.

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%.

Managing a Major Lube Oil Leak on a 150 MW Steam Turbine Power Plant

This paper focuses on the management of a major lube oil leak from bearing box No. 2 at the Sumsel-5 Power Plant. The steam turbine, a super high-pressure has been operational since April 2016. Overhauls in 2019 and 2023 addressed issues such as wear on oil deflectors, abnormal gland sealing clearances, and oil sealing wear. However, oil leakage persisted, necessitating further interventions. Rectification efforts included replacing oil deflectors, adjusting clearance, installing additional venting lines, the addition of a fiber gasket at bearing box No. 2 aimed to reduce and manage oil leakage. These measures resulted in a significant reduction in the oil leakage rate, from approximately 2826 ml/hr to 75 ml/hr during full load operation, representing a decrement of around 97.3%. Additionally, the frequency of lube oil top-ups decreased dramatically, leading to substantial cost savings for the plant. From a safety and environmental perspective, rectification measures mitigated fire hazards and reduced oil wastage, minimizing environmental impact. Regular monitoring of lubrication oil quality further ensures operational reliability. Although complete elimination of the oil leak was not achieved, the turbine continues to operate smoothly under manageable oil leakage conditions.

Research on the hydraulic parameters and leak rate of natural gas flow in natural gas pipelines based on the methods of characteristic line and successive approximation

Pressure and flow rate are the most important hydraulic parameters in natural gas pipeline flow, and leak rate is the most crucial parameter after a leak accident occurs. The study of these parameters is vital to the safey of natural gas pipelines and risk assessment after accidents. In this research, based on the conservation laws universally applicable to the motion of objects, we establish the fundamental control equation group for natural gas flow. Then, for both normal and leak conditions of pipelines, we use the characteristic line method to derive the corresponding difference equations for the fundamental control equations, thereby providing calculation methods for pressure and flow rate. Finally, we investigate the calculation method of natural gas pipeline leak rate under different leak aperture sizes, and validate the accuracy of this method through simulation examples.

Classification modeling of valve internal leakage acoustic emission signals based on optimal wavelet scattering coefficients

In order to achieve acoustic emission detection in valve internal leakage, it is essential to extract features, and establish an accurate mathematical model. Current valve internal leakage acoustic emission signal (VILAES) classification models mostly rely on human experience for selecting features, resulting in low accuracy for small leakage conditions. This paper combines the wavelet scattering transform (WST), Relief-F algorithm and AdaBoost.M1 algorithm, and proposes to use the optimal wavelet scattering coefficients as features to establish the VILAES classification model accurately for small leakage. Firstly, the first three order wavelet scattering coefficients of the VILAES are automatically extracted using WST and are transformed into one-dimensional features. Secondly, the Relief-F algorithm is employed to select the optimal feature subset. Finally, the optimal wavelet scattering coefficients and pressures are used as inputs to establish a classification model for VILAES and achieve an accuracy over 96.80% for small leakage.

Ultra-high-precision pneumatic force servo system based on a novel improved particle swarm optimization algorithm integrating Gaussian mutation and fuzzy theory

In this study, an ultra-high-precision pneumatic force servo system (UPFSS) is proposed. On the one hand, a novel air-floating pneumatic cylinder (AFPC) with an air-floating piston capable of independent air supply and exhaust is developed for this system, and its special flow channel design allows the air-floating piston to be suspended in the cylinder without being constrained by the pressure in the chambers. The friction force of the AFPC is less than 0.0049 N. On the other hand, a leakage chamber is constructed to simulate the clearance between the air-floating piston and the cylinder wall, and a fuzzy proportional integral (FPI)-based pressure control system (PCS) is designed for the simulated leakage chamber. Furthermore, a novel particle swarm optimization algorithm integrating Gaussian mutation and fuzzy theory (IGF-PSO) is presented. After testing, the IGF-PSO algorithm is found to have outstanding optimization performance. Then, the parameters of the FPI controller are optimized through the IGFPSO algorithm. Experimental comparisons reveal that the steady-state error achieved by the parameter-optimized pressure controller in response to the leakage condition is about 38 % smaller than that achieved by the pressure controller with parameters obtained using the trial-and-error method. Finally, the UPFSS is tested by using the optimized PCS to supply compressed air to the chamber of the AFPC. The results show that the UPFSS achieves a steady-state error of no more than 0.0279 N in the continuous step response within the range of 240 N.

Semi-supervised approach using Transductive SVM for internal leakage detection in two-stage hydraulic cylinder

Abstract Hydraulic cylinders with higher stages of extraction are extensively used in earthmoving and heavy machines due to their longer stroke, shorter retracted length and high-end performance. The rigorous and long hours of operations make cylinders prone to internal leakage, which visually remains unnoticeable This manuscript presents the conceptualization and realization of a newly developed 210 bar high-pressure hydraulic test rig actuated by a two-stage hydraulic cylinder. Experiments have been carried out to acquire pressure signals for two different leakage conditions (3 and 5% for moderate and severe leakage respectively) in the ramp wave motion of the cylinder. A decline in the working pressure and the piston velocity by approximately 10 and 45% for these leakage conditions respectively is noted. The time-frequency analysis infers these signals contain low-frequency components. For the automated leakage detection, a new iterative probability-based, transductive semi-supervised Support Vector Machine (TS-SVM) is proposed capable of learning with limited datasets in several iterations. TS-SVM classifies the internal leakage with 100% accuracy in 4 iterations and utilises only 64% of the total training data.

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling

Leakage from the overhead hydrogen piping (OHP) must be contained because of the wide flammability range and low minimum ignition energy of hydrogen; rapid leakage detection and appropriate emergency responses are necessary to ensure safe OHP operation. Consequence analysis after leakage detection is useful for hazardous-area estimation and assists operators in emergency-response planning. However, consequence analysis and leakage-detection methods have not been combined so far, and an integrated method has not yet been developed. This study aimed to develop a method based on machine learning (ML), physical modeling, and consequence analyses for rapid and appropriate decision-making after hydrogen leakage. Initially, an OHP model was constructed using physical modeling; this is a method that models target systems based on fundamental physical equations. Subsequently, an ML model for leakage detection was constructed based on various pressure or flow-rate profiles obtained from the OHP model. Regression models were constructed to estimate the leakage diameter. In addition, consequence analyses were conducted to estimate the effects of hydrogen explosions and fires using the leakage diameters predicted by the regression model. We confirmed that the ML model could distinguish between leakage and non-leakage conditions, and the estimated consequences could be used to visualize the risk level of hazardous areas near the leakage points in the case studies. The results of the case studies demonstrated the effectiveness of the proposed method for rapid decision making when hydrogen leaks from OHP. This method enables improved emergency responses in post-leakage situations.

Accuracy analysis of hydrogen leakage scale modeling based on similarity theory

The scale modeling is widely used in the study of hydrogen leakage diffusion, however the similarity between the hydrogen leakage scale modeling and the full-scale model remains incomplete. This article established a full-scale and a 1/5th scale model and verify the accuracy relationship between the two models. Firstly, by comparing the full-scale model with only changing the size simulation, it was found that the accuracy of the two models was poor. However, after using the similarity theory to change the initial leakage conditions, the overall overlap between the full-scale and the scale modeling was improved apparently. Under the initial conditions of this study, the combustible cloud maps of the two models that only changed in size exhibit poor coincidence. The results show that the equivalent performance of a scale modeling instead of a full-scale model can be greatly improved by using the similarity theory. The findings of this study can provide a valuable reference for experimental results based on the scale models to the full-scale models.

Privacy-preserving finite-time average consensus and its application to power allocation of battery energy storage systems

In this paper, the privacy-preserving problem of finite-time discrete-time average consensus is studied by adding random numbers. The privacy leakage conditions are given for systems with curious but not malicious nodes and eavesdroppers, respectively. By introducing the random numbers and node decomposition mechanism, the privacy information of the nodes cannot be estimated by the adversaries. The proposed algorithm is applied to the power allocation problem of distributed battery energy storage systems. Based on the previous proposed multi-rate sampling method, the privacy-preserving finite-time discrete-time average consensus is utilized to estimate the accurate average value within finite steps. The accurate power allocation based on the same relative SoC variation rate can be achieved with the privacy preservation of the transmission information. Finally, based on a networked battery energy storage system, simulation studies are presented to verify the effectiveness of the proposed method.

Aquifer-CO2 leak project. Effect of CO2-rich water percolation in porous limestone cores: Simulation of a leakage in a shallow carbonate freshwater aquifer

Carbon capture and storage (CCS) is a promising technology for reducing greenhouse gas emissions, however, leakage of CO2 constitutes a major concern for aquifers. Despite abundant literature on petrophysical and geochemical changes at storage conditions, few studies address the impact of CO2 leakage on petrophysical and at aquifer-like pressures, flow rates and temperatures. The aim of the paper is to study and quantify at the core scale, the effects of various factors, including flow rates, fluid salinities, CO2 concentrations and limestone carbonate facies, on the petrophysical and geochemical parameters of a carbonate freshwater aquifer during an experimental CO2 leakage. To achieve this, successive CO2-rich water percolation experiments were performed at the core scale, while monitoring changes in petrophysical parameters and water chemistry.During our experiments, initial permeability increments were observed for both rock samples, followed by decreases in later experiments. Evidence of pore clogging and wormholes was also observed. It was found that the transport of particles led to significant porosity creation. The water monitoring sensors were sensible to the experimental leakage conditions, particularly electrical conductivity. The results of this study contribute to the understanding of petrophysical changes in carbonate aquifer systems by anticipating which type of aquifers are more vulnerable to dissolution and to what extent the CO2 leakage conditions modify the petrophysical parameters.

Preliminary accident analysis of the loss of vacuum in vacuum vessel for the European DEMO HCPB blanket concept

Design basis accidents are investigated continuously for the European DEMO reactor accompanying its development. One selected postulated initial event (PIE) is a loss of vacuum (LOVA) in vacuum vessel (VV) with large ingress of air induced by rupture in a VV penetration. It has been investigated for the helium cooled pebble bed (HCPB) blanket concept according to the DEMO baseline 2017. The associated primary heat transfer system (PHTS) and the related systems in the tokamak building, from the VV to the PHTS vault and galleries, are considered for the investigation. The LOVA is postulated to occur at a port seal of the electron cyclotron equatorial port plug on the side of the closure plate with (i) a small leak of 1.0 × 10−3 m2, or (ii) a large break size of 1.0 × 10−2 m2. Air ingress from one port cell into the VV leads to the VV pressurization and the fusion power termination followed by an unmitigated plasma disruption. A loss of off-site power for 32 h is assumed to coincide with the disruption. An in-vessel loss of coolant accident (LOCA) is considered as a consequence if the affected first wall (FW) reaches the defined temperature of 1000 °C. The radioactive inventories in the VV (tritium, W-dust) can mobilize towards the VVPSS, the affected systems in the building and the environment due to pressurization, venting and leak conditions. MELCOR 1.8.6 for fusion is applied for this deterministic safety analysis. The resulting releases of radioactivity to the environment are then provided for dose calculation using the computer systems UFOTRI and COSYMA. Outcomes of this LOVA analysis are critically discussed: the transient evolutions of different cases are compared; hydrogen production is detected in case of aggravating FW failure; the source terms (tritium, W-dust) are transported to the connected systems; and the dose results from the environmental releases are provided.

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.