Leak Detection Method Research Articles

Leakage detection method of underground heating pipeline based on improved wavelet threshold function

For urban heating pipeline network, the intelligent and precise detection is an important guarantee for urban heating infrastructure of the safe and low-carbon operation. Heating pipe network was directly buried in the soil and difficult to detect the leakage point due to lack of non-excavation detection method for rapid repair. An improved noise reduction algorithm of the wavelet threshold function coupled with the acoustic method was used for the directly buried pipe leakage detection. Large-scale leakage experimental tests were used to validate the reliability. The influences of temperature, pressure, flowrates and leakage distance on the frequency and time domains were then investigated. Results show that the improved threshold function could well detect the leakage for directly buried hot water heating pipes. When the temperature increases, the leakage frequency domain spreads from 200 - 800 Hz to 50–1500 Hz. The pressure, flow rate, and leak point location could affect the amplitude feature. The accuracy ranges of the improved method in the different location, temperature and pressure and flowrates are separately 0.11–2.36%, 0.11–1.96%,0.11–3.49% and 0.16–1.55%, respectively.

Advanced acoustic leak detection in water distribution networks using integrated generative model

Water distribution networks (WDNs) experience significant water loss due to leaks, necessitating advanced water leak detection methods. However, machine learning-based acoustic method heavily relies on signal information and is limited by data scarcity and the limited diversity of available data. To address this challenge and enhance water leak detection in WDNs, this study proposes an LSTM-GAN approach. Acoustic signals are collected from WDNs to train the LSTM-GAN model, which generates synthetic leak signals to enhance the dataset. The validity of the generative method is evaluated through t-SNE and acoustic characteristics analysis. LSTM-based water leak detection models are established and compared using the original and the generated datasets to confirm the efficacy of generated samples in improving water leak detection performances. The capability of LSTM-GAN has been evaluated through different perspectives, including sensitivity analysis and model comparison. The results validate the quality and consistency of the generated acoustic signals under leak conditions. Besides, the optimal number of generated samples should be determined according to the requirements and characteristics of the leak detection task. Furthermore, the comparison between the proposed method and other acoustic generative methods demonstrates the superiority of LSTM-GAN-generated signals in enhancing the performance of leak detection models. The proposed generative method offers an innovative approach to facilitate machine learning-based leak detection models with limited data, thereby enhancing robustness.

Combining Cylindrical Voxel and Mask R-CNN for Automatic Detection of Water Leakages in Shield Tunnel Point Clouds

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.

Lost circulation detection method based on cepstrum analysis of transient pressure waves

Encountering natural fractures or unbalanced formation pressure during oil drilling can result in economic loss and environmental pollution due to well leakage. Existing detection methods encounter challenges such as high costs, complex downhole environments, and difficult data acquisition. To address these issues, we propose a well leakage detection method using cepstrum for analyzing transient pressure waves. Cepstrum is a signal Fourier transform after logarithmic operation and then Fourier inverse spectrum obtained. By studying the propagation of transient pressure waves in the wellbore, we identify drilling fluid leakage location and amount based on time-dependent and amplitude changes of pressure wave signal characteristic peaks. To handle noise in the pressure wave signal, we employ adaptive noise-complete ensemble empirical modal decomposition (CEEMDAN) and wavelet threshold (WT) joint denoising. Correlation coefficient (CCF) with the Hilbert joint spectrum (HJS) is used to extract main frequency components, achieving denoising. Experimental results confirm: ① Noise interference in transient pressure waves is effectively suppressed using the CEEMDAN-WT-CCF-HJS denoising method. ② Cepstrum analysis of the pressure wave signal during wellbore annulus system leakage reveals distinct reflected wave characteristic peaks, aiding in locating different leakage points, with the amplitude of these peaks reflecting the size of the leakage. ③ This method efficiently utilizes time-frequency information from the excitation pressure wave signal, offering advantages over traditional time-domain and frequency-domain analysis. Experiments covering various leakage scenarios, amounts, and borehole sizes yielded controlled experimental errors (2.25%–9.10%), within a reasonable range. The method's validity and reliability were confirmed, providing theoretical support and technical guidance for well leakage detection in oil drilling.

Validation research of pressure decay test method for internal leakage detection of hydraulic cylinder

Leakage test is one of the acceptance tests to ensure hydraulic cylinder reliability. Under the consideration of economic and technical maturity conditions, the existing internal leakage detection methods may be difficult to achieve rapid screening. After research, the pressure decay test method is a common method of quickly testing hydraulic cylinders for internal leakage in the manufacturing industry. However, the validity of the pressure decay test method has not been theoretically verified, limiting the versatility of the method. This paper will theoretically validate the effectiveness of the pressure decay test method and provide important theoretical support for its use as a general rapid screening method for internal leakage, thereby improving the reliability and safety of hydraulic systems. Both simulation and experimental results show that there is a linear relationship between the pressure decay rate and the internal leakage rate. Especially for working pressures greater than 16 MPa, the pressure factor has an insignificant impact on the application of this method.

Refrigerant leak detection in industrial vapor compression refrigeration systems using machine learning

Efficient detection of refrigerant leakage is of utmost importance for industrial refrigeration systems due to its potential to cause substantial impacts on system performance and the environment. Existing research on fault detection and diagnosis in refrigeration systems primarily revolves around solutions based on experimental or laboratory data. However, in the industrial use case, achieving accurate and early detection poses significant challenges. This paper reports on the development of a novel refrigerant leak detection method for industrial vapor compression refrigeration systems. Our method leverages real-world data obtained from operational installations, enabling us to assess its reliability and applicability. The proposed data-driven approach involves predicting the fault-free liquid level in the installation receiver and comparing the actual and predicted levels. In this work, we place emphasis on features and model selection. Dedicated metrics combined with a model comparison method are proposed to evaluate and compare the performance of commonly used regression models with two sets of features to determine the most effective one. Furthermore, we provide insights into the results obtained from the deployment of the proposed method in real-world industrial installations.

Liquid-filled pipeline leak detection and localization based on multi-scale residual networks

Effective ways to improve the accuracy of liquid-filled pipeline leak detection are one of the key issues that need to be addressed urgently in a conservation-oriented society. Recently, pipeline leak detection methods based on deep learning have developed rapidly. To improve the learning ability of convolutional neural network for pipeline leak signal features and leak detection accuracy, a multi-scale residual networks (MSRNs) model is proposed in this paper for liquid-filled pipeline leak detection and localization. The model uses convolutional kernels of different scales to extract multiscale features of pipeline leakage signals based on deep residual networks (DRNs) and uses fully connected layers to fuse the features, thus improving the accuracy of pipeline leakage detection and localization. Among them, the large convolution kernel can acquire the low-frequency information of the signal due to its sizable perceptual field, the medium convolution kernel can capture the local and global features of the signal, and the small convolution kernel is more sensitive to the high-frequency information of the signal. Meanwhile, a pipeline leakage test platform is built to evaluate the proposed model. The test results show that the accuracy of leak detection and localization of MSRN model is 98.3%, which is better than that of single-scale DRN model. In addition, the proposed MSRN model is verified to have good generalization and noise immunity through testing and analyzing the leakage signals under different pressures and background noises.

Gas pipeline leakage detection and location by using w-FBG array based micro-strain sensing technology

In addressing the significant security concerns associated with urban natural gas transportation, particularly the potential catastrophic consequences of leaks, this paper introduces a novel approach for detecting and locating leaks in natural gas pipelines using weak fiber Bragg grating (w-FBG) array micro-strain sensing technology. The field pipeline model is established, w-FBG array is applied to pipeline leakage experiment, and the pipeline leakage is detected by analyzing the change trend of micro-strain. The pipeline leakage experiments under different pressures were carried out. The experimental results show that there is a good linear relationship between the pipeline leakage pressure and the wavelength change of w-FBG, and the linearity is greater than 0.99, which is consistent with the theoretical analysis and verifies the applicability of w-FBG. According to the characteristics of micro-strain transfer in pipeline leakage process, a ‘abrupt change point’ capturing method based on peak to peak slope and threshold judgment is proposed. Results indicate that the proposed algorithm effectively detects pipeline leaks and accurately locates the point of leakage, with a positioning error of approximately 0.5 m. Compared with the traditional method, the proposed method has higher precision.

A Unified Spatial-Pressure Sensitivity Partitioning and Leakage Detection Method within a Deep Learning Framework

This study introduces an innovative approach for leak detection in water distribution systems (WDSs), integrating three-order embedding, k-means clustering, and long short-term memory (LSTM) networks, with pressure-sensitive analysis techniques. This comprehensive methodology segments the network into distinct partitions, utilizes simulated leak events to train the deep learning networks, and establishes a sophisticated model for accurately identifying leak partitions. This approach generates a leak dataset by adjusting water demands, which could effectively pinpoint the leaks in a specific partition by leveraging both the pressure sensitivity and spatial coordinates of nodes, allowing for the elimination of the need for manual work and precise identification of leaks in targeted areas. Through the analysis of two case studies, the model demonstrates its ability to effectively pinpoint potential leak partitions, significantly enhancing operational efficiency and reliability in managing the complex problems of urban water resource management. This approach not only optimizes leak detection but also paves the way for advanced, data-driven strategies in WDSs, ensuring sustainable and secure water distribution in urban settings.

An intelligent water supply pipeline leakage detection method based on SV-WTBSVM

Water supply pipeline leakage not only wastes resources but also causes dangerous accidents. Therefore, detecting the state of pipelines is a critical task. With the expansion of the scale of the water supply pipeline, the amount of data collected by the leak detection system is gradually increasing. Moreover, there is an imbalance of sample in the data. This makes the detection performance of traditional leakage detection methods deteriorate. To solve the above issues, this paper proposes a pipeline leakage intelligent detection method based on a support vector weighted twin-bound support vector machine (SV-WTBSVM). Noise in the data negatively affects the performance of the classifier. To eliminate the effect of noise, a hybrid denoising algorithm based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used for denoising to filter out the noise in the data. Twin bound support vector machine (TBSVM) is a classical classification algorithm that has been widely used in the detection of pipeline leakage. To solve the decrease in classification accuracy caused by sample imbalance, the SV-WTBSVM algorithm oversamples the minority class samples based on the distance density and integrally undersamples the majority class samples to obtain a balanced sample. Since pipelines often have multiple working conditions, the SV-WTBSVM used for binary classification cannot meet this requirement, and this paper combines the SV-WTBSVM with the ‘one-to-one’ strategy to address the multi-classification problem. Finally, experiments have verified that the SV-WTBSVM algorithm not only retains the advantages of fast training speed and simple operation of the TBSVM but also improves the classification accuracy and generalization ability of the algorithm when dealing with imbalanced data.

Simulation and experimental study of buried natural gas pipeline leak detection based on sound source characteristics

Buried pipe-line leakage will affect the thermal characteristics of the soil environment, leading to a poor soil environment. In addition, leakage of natural gas possibly produces an explosion and subsequent fire, which has fatal harm. Sustainable detection of underground gas pipe-line leaks is a significant part of current research. In this study, a method for leak detection of buried natural gas polyethylene pipe-lines based on sound source characteristics is investigated. The simulation software was applied in analyzing the variation of leakage rate and sound source in buried pipes under different leakage conditions including mainly different leakage apertures and pipe pressures. Also, an experiment platform was built to verify the simulation results. These results can provide help for gas pipe-line leakage detection and safety protection.

Qualification of Compensatory Test Methods Involving Pressure Increase in the VVER Containment System

In general, containment leakage testing of nuclear power plants with VVER reactors by elevated pressures using the compensatory leakage detection method (CLDM) can be performed at pressures and pressure increase rates higher than those stipulated by the regulations using the absolute pressure method (AP method). Elevated pressures and pressure increase rates under certain conditions can violate safety limits of the containment systems and/or decrease the reliability/life of containment structures and equipment. These factors determine the need to qualify the CLDM to promote conditions for reliability and safety. A non-stationary thermodynamic model of the qualification conditions for CLDM testing of the containment was developed. The criteria for CLDM qualification conditions are the maximum allowable pressure and pressure increase rate during testing. The CLDM condition for recording leakage in the containment is pressure stabilization in the containment systems. Based on the developed CLDM thermodynamic model, it was established that the containment leakage rate is determined by the flow rate of air entering the containment systems and by the thermodynamic state of the air inside and outside the containment systems. The established qualification conditions were used to determine conditions for the minimum recorded leakage sizes within CLDM and the maximum allowable ambient air flow rates and test duration. A prerequisite for justifying the qualification and implementation of the CLDM is to revise/amend regulatory and technical requirements for the maximum allowable pressure and pressure change rate in the containment systems and for the conditions for disconnecting containment systems passive heat removal systems (if any) during testing.

Novel EMD with Optimal Mode Selector, MFCC, and 2DCNN for Leak Detection and Localization in Water Pipeline

Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods’ complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method’s leak detection-only credibility. This paper also implements EMD variants to show the novel EMD’s importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method’s prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%.

3D Simulation and Leakage Detection Technique for Deep Foundation Pits Based on Electric Field

To solve the problems of poor accuracy, poor anti-interference ability and unclear distribution of leakage electric field response characteristics of the traditional ground DC method for leakage detection of underground diaphragm walls in foundation pits, this paper proposes a deep foundation pit leakage detection technology based on the electric field. Secondly, it constructed the multi-field coupling equations and boundary conditions of the leakage electric field coupled with the leakage flow field and ion diffusion field, and then realized the three-dimensional forward evolution of the leakage electric field of the pit using the nonstructural mesh dissection method and the finite-element-infinite-element discretization method. Finally, physical model experiments and numerical simulations are used to confirm the reliability and accuracy of the foundation pit leakage detection technology and system. The results shows that monitoring the continuous change of the electric field for a period of time, the electric field detection technology for foundation pit leakage can effectively identify the location of hidden leakage and overcome the site’s strong industrial interference, resulting in a new technical solution for foundation pit leakage detection.

Optimization and testing of a Mass Spectrometer Leak Detection (MSLD) system

A rapid helium mass spectrometry leak detection method is established to respond to problems of low sensitivity and efficiency in detecting leaks in large vessels. The optimized multistage tandem leak detection system improves leak detection sensitivity by increasing the amount of gas entering the analysis chamber. By involving the main pumping system in the detection process, the effective pumping speed is increased, resulting in a significant reduction in system response time. Additionally, the optimal leak detection vacuum required for maximizing the leak velocity indicating gas movement is determined, thereby further decreasing the system’s response time. Our application demonstrates that the optimized leak detection system increases sensitivity by one to two orders of magnitude, reduces system response time by over 90%, and shortens evacuation time by 7 h. Ultimately, the optimized design of the vacuum system developed for large vessels demonstrates greatly improved leak detection efficiency and overall capability.

Frost-Preventing Design for Space Station Refrigerator

Different preservation temperatures for sample storage in space stations and corresponding refrigerator devices have been designed. In the below-freezing environment, frost will occur on the inner wall surface and the sealing place of the refrigerator device, which may have an impact on the operating performance and sealing performance of the refrigerator device. This paper analyzes the impact of frost on refrigerator storage devices, and designs and verifies a scheme of air cooling with a hollow seal method to reduce the impact of frost. The designed refrigerator was tested for air cooling, sealing, and long-term performance. The tests show that the air-cooling structure designed in this paper can effectively reduce the frost on the sample surface. A cumulative helium mass spectrometry leak detection method is applied to verify that the hollow seal structure designed has a better sealing property, which can prevent the diffusion of water vapor and frosting at the sealing position. The design with air cooling and hollow sealing has less frost and more stable performance for long-term operation. The results demonstrate this refrigerator would have the capability of operating on-orbit. The frost-preventing method for space station refrigerator designed in this paper has significance for the design of similar cryogenic equipment in microgravity environments.

A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov–Smirnov Test

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

Smart Buildings: Water Leakage Detection Using TinyML.

The escalating global water usage and the increasing strain on major cities due to water shortages highlights the critical need for efficient water management practices. In water-stressed regions worldwide, significant water wastage is primarily attributed to leakages, inefficient use, and aging infrastructure. Undetected water leakages in buildings' pipelines contribute to the water waste problem. To address this issue, an effective water leak detection method is required. In this paper, we explore the application of edge computing in smart buildings to enhance water management. By integrating sensors and embedded Machine Learning models, known as TinyML, smart water management systems can collect real-time data, analyze it, and make accurate decisions for efficient water utilization. The transition to TinyML enables faster and more cost-effective local decision-making, reducing the dependence on centralized entities. In this work, we propose a solution that can be adapted for effective leakage detection in real-world scenarios with minimum human intervention using TinyML. We follow an approach that is similar to a typical machine learning lifecycle in production, spanning stages including data collection, training, hyperparameter tuning, offline evaluation and model optimization for on-device resource efficiency before deployment. In this work, we considered an existing water leakage acoustic dataset for polyvinyl chloride pipelines. To prepare the acoustic data for analysis, we performed preprocessing to transform it into scalograms. We devised a water leak detection method by applying transfer learning to five distinct Convolutional Neural Network (CNN) variants, which are namely EfficientNet, ResNet, AlexNet, MobileNet V1, and MobileNet V2. The CNN models were found to be able to detect leakages where a maximum testing accuracy, recall, precision, and F1 score of 97.45%, 98.57%, 96.70%, and 97.63%, respectively, were observed using the EfficientNet model. To enable seamless deployment on the Arduino Nano 33 BLE edge device, the EfficientNet model is compressed using quantization resulting in a low inference time of 1932 ms, a peak RAM usage of 255.3 kilobytes, and a flash usage requirement of merely 48.7 kilobytes.

An angle-based leak detection method using pressure sensors in water distribution networks

Abstract Leak detection has significant implications for the long-term stable operation of water distribution networks (WDNs). This study presented a novel leak detection method by calculating the angular variance between a pressure vector and other vectors in the database, to evaluate the presence of an anomaly in a network. The top priority for this method was to establish a reliable dataset collected from the pressure sensors, which is generated by EPANET 2.2. Numerous node water demand data in normal conditions were generated by the Monte Carlo method, and leak conditions with various leak flows were simulated by creating leak holes in the pipes. Through learning the composite normal and abnormal data in a certain proportion, the angle-based outlier detection model was employed to identify abnormal events. This angle-based method was applied in an actual WDN and the identification performance for anomalies was compared with that of previous detection methods. The results indicated that the novel method proposed in this study could significantly improve the accuracy and efficiency of leak detection compared to the threshold-based and distance-based detection methods.

Machine Learning Model for Leak Detection Using Water Pipeline Vibration Sensor

Water leakage from aging water and wastewater pipes is a persistent problem, necessitating the improvement of existing leak detection and response methods. In this study, we conducted an analysis of essential features based on data collected from leak detection sensors installed at water meter boxes and water outlets of pipelines. The water pipeline data collected through the vibration sensor were preprocessed by converting it into a tabular form by frequency band and applied to various machine learning models. The characteristics of each model were analyzed, and XGBoost was selected as the most suitable leak detection model with a high accuracy of 99.79%. These systems can effectively reduce leak detection and response time, minimize water waste, and minimize economic losses. Additionally, this technology can be applied to various fields that utilize water pipes, making it widely applicable.