Leak Detection In Pipes Research Articles

Hydraulics-based pipe leak detection revisited and experimentally verified

Hydraulics-based pipe leak detection revisited and experimentally verified

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.

Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection

Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection

On the mixed acoustic and vibration sensors for the cross-correlation analysis of pipe leakage signals

Pipe leak detection can be carried out by various acoustic/vibration sensors, such as acoustic pressure, velocity and acceleration sensors. The purpose of this study is to investigate the effectiveness of the mixed acoustic/vibration sensors for the determination of pipe leakage. Firstly, a propagation and attenuation model of leakage signal in the liquid-filled pipeline is established based on frequency response functions. Further, the expressions of the cross-correlation function of different sensor types are derived with respect to the differential properties of Fourier transform. Then, the influence of signal-to-noise ratio (SNR) on the cross-correlation function is analyzed. Numerical simulation is employed to generate pressure, velocity and acceleration signals by differentiating the leakage source signals. Simulation results indicate that the use of pressure sensor acts as low-pass filtering operation, whereas the velocity and acceleration sensors act as band-pass filtering. Finally, experimental work is carried out on a medium density polyethylene (MDPE) pipe. Leakage signals are collected synchronously by hydrophones and accelerometers. Experimental results show that the cross-correlation function of hydrophones fluctuates slowly with peak value being less obvious, while the cross-correlation function of accelerometers fluctuates rapidly and has a sharper peak. In comparison, there is a compromise in the fluctuation for the sake of peak sharpness in the cross-correlation function of the mixed sensors, indicating that the corresponding cross-correlation result are less affected by background noise.

A convolutional neural network for pipe crack and leak detection in smart water network

The implementation of a smart water network (SWN) is viewed as a strategic approach to address many challenges faced by water utilities, such as pipe leak detection and main break prevention. This paper develops a convolutional neural network (CNN)–based model to classify acoustic wave files collected by the South Australian Water Corporation’s (SA Water’s) SWN over the city of Adelaide. The VGGish model (VGG refers to the team who developed the model—Visual Geometry Group) is selected as a suitable transfer learning model to extract features from wave files. The CNN model classifies an acoustic wave file as an anomaly or other background or environmental noise. Identification of a wave file as an anomaly triggers a Siamese CNN model to determine whether it is related to a regular/irregular scheduled event (for example, irrigation system near public parks or water consumption by large buildings). A field investigation is initiated if a wave file is classified as an anomaly and it is not related to a scheduled event. The developed models have been validated using data that is recorded by SWN in Adelaide. This validation data set comprises 1098 wave files, which are recorded by 34 accelerometers and are associated with 32 known leaks. The validation results shown that accuracy of alarms generated by the developed models is 92.44%. The validations confirm the developed models as an effective tool for water pipeline leak and crack detection, which, in turn, enables proactive management of the pipeline assets.

Evaluation of the Factors Impacting the Water Pipe Leak Detection Ability of GPR, Infrared Cameras, and Spectrometers under Controlled Conditions

Leakages from water distribution infrastructures are responsible for a considerable amount of water losses. Consequently, innovative, non-destructive techniques (NDT) of leakage detection for early recognition is vital. In this study, the leak detection abilities of Ground Penetration Radar (GPR), spectrometers, and infrared (IR) cameras were investigated, and the factors affecting the leak detection response were assessed. Three experimental setups were constructed to simulate underground pipes in dune sand. Three different pipe materials (PVC, PPR, and PE) were investigated under three levels of moisture content (MC; 2%, 5%, and 10%) and three types of leaks (hole, crack, and faulty joint). The IR camera was the most effective among the different NDTs used in this study. The IR camera detected all types of leaks at 2% and 5% MC but was inconclusive at 10% MC. Similarly, the GPR detected the leaks at 2% and 5% MC successfully but was inconclusive at 10% MC. Lastly, the spectrometer was the least reliable NDT to detect leaks. Results suggest that IR cameras and spectrometers that operate in the mid-IR range (5–25 µm) can detect leaks.

Non-invasive leakage detection & localisation technique in noisy industrial environment

An innovative non-invasive method for pipe leak detection and localization in noisy environment is presented in this paper. Nowadays, it is well known that complex pipeline networks are used for operational purposes and fluid transportation in every high-end-technology heavy industry such as refineries, combined heat and power cycles, cement and steel industries, etc. In all these cases, safety is the key parameter in order to ensure the efficient plant operation and to avoid any possible accident with devastating consequences that may lead to a turn down of the production process. For this reason, it is mandatory to develop reliable enough methods for detection of fluid leakages which represent the most common threaten in pipeline networks. Towards to this direction an integrated experimental setup was built in order to validate the results of the algorithm which was also developed for the purposes of the present study and aims to detect and locate the artificial leakages through the attenuation of the acoustic signal propagating in a pipeline. This experimental setup consists of pipelines that installed into an anechoic chamber and uses pure water as working medium. Apart from the high-efficient accuracy of the developed algorithm in the leakage detection and localization, the proposed method was designed with extra focus on the reduced CAPEX and OPEX costs. Finally, according to the results the proposed system gives sufficiently low false alarm regarding the leakage detection, while the mean percentage error of the leakage localization is around 6% which is considered as an acceptable value.

Efficient leak detection in single and branched polymeric pipeline systems by transient wave analysis

The widespread use of plastic pipes in different fluid conveyance systems has greatly driven the recent development and application of transient-based methods (TBMs) for leak detection in viscoelastic/polymeric pipelines. Current TBMs for viscoelastic pipe leak detection are usually achieved by a two-step procedure, namely viscoelastic parameters identification and leak detection, which requires the pre-knowledge of intact system states (i.e., non-leak) for comparative analysis. This paper presents an efficient single-step frequency domain inverse transient analysis (FDITA) method for simultaneous identifications of viscoelastic parameters and leaks in plastic pipes, so as to enhance the applicability and accuracy of TBMs. Both the single and branched polymeric pipe systems are applied for the method development and application. To this end, analytical solutions of single and branched systems from the transfer matrix method are firstly derived to represent the transient frequency responses of viscoelastic pipelines with leaks. A global optimized nonlinear curve fitting method is then employed to identify both viscoelastic parameters and potential leaks by knowing/measuring other system and flow conditions. Extensive experimental validations and numerical applications of both single and branched pipe systems demonstrate the very good efficiency and accuracy of the developed method for leak detection in different viscoelastic pipe systems. Furthermore, the mechanism of transient wave-leak-viscoelasticity is analysed based on these application results and theoretical evidence. Finally, a sensitivity analysis is performed to quantify and discuss the advantages and potential limitations of the developed method in the paper.

Water Pipe Leakage Detector Using a Pressure Transmitter Sensor with a Remote Distance Smartphone Display

This research aimed to design the air pipe leak detection using a pressure transmitter sensor with a smartphone as a display. The research produced a method that can be relied upon by pipe leaks quickly and accurately. The method was carried out by using two flowmeter sensors that are placed before and after the pipe leak point to record data on the difference between intake and exit air flow (ΔQ). The resulting data was transmitted to the computer using a TCP/IP-based network. The results obtained show that the smaller the difference in the flow of air into and out (ΔQ), the farther the pipe leak is (X).

Design of Internet of Things Based Smart and Efficient Water Distribution System for Urban and Agriculture Areas

Groundwater plays an important part in India’s economy. It provides water to about 85% urban and rural proportion for household and agriculture needs. India being the 2nd largest in world population with a diverse population that is three times the size of the US but one-third the physical size. World Bank according to, how many people are living in poverty is still extremely disproportionate to the number of middle-income people, with a combined rate of over 52 percent of rural and urban poor. One of the major drawback in the Indian water distribution systems was found to be loss of water resource due to leakage of pipes. India experiences both floods and droughts periodically. Consequently, innovations in pipe leak detection recognition and reparation methods are being implemented. But still identifying the leaks at a early point is vital in order to ensure minimal water wastage in distribution systems. A major component of pipe leak detection is the ability to reliably locate the place of leakage in pipes by minimal invasion. Water transport industry is increasingly concerned about leakages in water delivery pipelines. To pacify them and thereby supporting the economy by saving the water resources with proper and sufficient utilization of the same, this paper projects the solution to overcome pipe leakage issues with IoT dependent smart water monitoring system.

Rancang Bangun Sistem Monitoring dan Pendeteksi Lokasi Kebocoran Pipa Berdasarkan Analisis Debit Air Berbasis IoT

The purpose of this research is to design and realize a pipe leak detection system that can be monitored with an Android application. Water flow sensor is used to determine the flow of water, with water flow rate analysis it is possible to know the leakage area, pipe leakage rate, and the number of leaks in pipe area if there is a water flow rate decrease. The data is processed with an NodeMCU microcontroller based on Internet of Things (IoT). The accuracy of all water flow sensors after calibration is 97,53%. When testing with the determination of each leaked area the results are appropriate. The reading of each water flow rate decrease that occurs has an accuracy of determiningthe leakage rate of 90,2% while the suitability of reading the number of leakage areas depends on the detection of the leakage area and the classification of the leakage rate. The greater the water flow reads on the sensors, the precission level to determine the level of water pipe leakage is getting better, and vice versa, this is due to sensors having variable variations in flow rate readings.

Pipeline leak localization using matched-field processing incorporating prior information of modeling error

To date, the use of matched-field processing (MFP) for leak detection in pipes has been limited to cases where the mismatch between data and model is assumed to be random and Gaussian distributed. This paper extends the MFP to the more realistic case where the mismatch involves both random and modeling errors. Experimental results show that the modeling error for cases with and without a leak remains similar in shape and magnitude. As a result, modeling errors can potentially be estimated from a baseline signal which ideally can be obtained before major defects emerge. This attribute is exploited to formulate a novel MFP technique that uses both past baseline and current signals to detect leaks. The novel MFP remains optimal in the sense of achieving maximum signal-to-noise ratio. The gain of the proposed leak detection method is assessed via three experimental scenarios in which the modeling errors range from simple to complex.

Leak detection in a pipe using Fibre Bragg Grating sensor

Being the main and safest oil and gas transportation method globally, the integrity and condition of pipeline system is very crucial. The offline existing maintenance method might have consumed more time and risky as it does not detect the flaws immediately after it is formed. This paper focused on the detection of the leak detection in pipes using the Fiber Bragg Grating pressure transducer. Two different sizes of artificial leak were introduced on the pipe in order to measure the applicability of the FBG sensor in detecting the leak in a pipe. The FBG signals were acquired on a galvanized steel pipe in lab scale size equipped with 5 pairs of valve. During the experimental work execution, the FBG sensor was attached on the pressure transducer that is linked to the pipeline. Findings showed that bigger leak size contributed to higher voltage drop in the pipe which was 0.0075 V compared to 0.0017 V. This study shows that FBG is a reliable method to be utilised in leak detection because it is very sensitive towards pressure changes in pipe. The results also showed that FBG exhibits good uniformity.

A deep learning approach for motion segment estimation for pipe leak detection robot

The trajectory motion of a robot can be a valuable information to estimate the localization of an autonomous robotic system, especially in a very dynamic but structurally-known environments like water pipes where the sensor readings are not reliable. The main focus of this research is to estimate the location of meso-scale robots using a deep-learning-based motion trajectory segment detection system from recorded sensory measurements while the robot travels through a pipe system. The idea is based on the classification of the motion measurements, acquired by inertial measurement unit (IMU), by exploiting the deep learning approach. Proposed idea and utilized methodology are explained in the related sections and it is observed that convolutional neural network approach is quite powerful to overcome the unreliability of IMU data.

Locating Leaking Buried Pipes Based on Ground Microseismic Records in 3D Space

Pipelines are a safe energy-efficient mode of transporting fluids and gases. However, risks of pipe leaks still exist. Various pipe leak detection methods are in use; some traditional methods rely on acoustic receivers that must be located on the pipe, thereby restricting their implementation. Microseismic monitoring resolves this restriction because the ground rather than the pipe is monitored. However, microseismic monitoring has, thus far, only been applied using 2D methods for pipeline leak detection, and its accuracy is limited. To address this problem, a new 3D leak detection and location method for buried pipelines is proposed. First, using a 3D staggered-grid finite-difference method for acoustic equations, we simulated a microseismic wave field generated by a leaking pipeline, and recorded it with a ground surface geophone array. Then, a generalized cross-correlation technique was used to extract the time difference. Finally, the simulated annealing method was used to obtain the leak location in 3D space. Simulation and field data results show that the proposed method can locate the leakage point and has good anti-noise capability. Accurate 3D leak location requires at least two survey lines. Non-parallel survey lines can locate the approximate plane position of the leakage. Parallel line and orthogonal line pairs can be used to obtain the leak position accurately; the parallel line pair is the most accurate. The proposed method was concluded to be effective and accurate at detecting and locating leakages in buried pipeline networks.

Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms

ABSTRACTThis paper presents the validation of a novel leak detection method for water distribution pipelines, although it could be applied to any buried pressurized fluid flow pipe. The detection method is based on a relative pressure sensor attached non-invasively to the outside of the pipe combined with temperature difference measurements between the pipe wall and the soil. Moreover, this paper proposes an anomaly detection algorithm, originally developed for monitoring website traffic data, which differentiates a ‘leak’ event from ‘normal’ pressure change events. It is compared to two more commonly used methods based on a fixed threshold and a moving average. The validation of the new system in a field trial over a 6-month period showed that all the known leaks were identified with 98.45% accuracy, with the anomaly detection algorithm performing best, making this system a real contender for leak detection in pipes.

Accuracy and Sensitivity Evaluation of TFR Method for Leak Detection in Multiple-Pipeline Water Supply Systems

The transient frequency response (TFR) based pipe leak detection method has been developed and applied to water pipeline systems with different connection complexities such as branched and looped pipe networks. Previous development and preliminary applications have demonstrated the advantages of high efficiency and non-intrusion for this TFR method. Despite of the successful validations through extensive numerical applications in the literature, this type of method has not yet been examined systematically for its inherent characteristics and application accuracy under different system and flow conditions. This paper investigates the influences of the analytical approximations and assumptions originated from the method development process and the impacts of different uncertainty factors in practical application systems on the accuracy and applicability of the TFR method. The influence factors considered for the analysis contain system properties, derivation approximations and data measurement, and the pipeline systems used for the investigation include simple branched and looped multi-pipe networks. The methods of analytical analysis and numerical simulations are adopted for the investigation. The accuracy and sensitivity of the TFR method is evaluated for different factors and system conditions in this study. The results and findings are useful to understand the validity range and sensitivity of the TFR-based method, so as to better apply this efficient and non-intrusive method in practical pipeline systems.

Evaluation of Piezodiagnostics Approach for Leaks Detection in a Pipe Loop

Pipe leaks detection has a great economic, environmental and safety impact. Although several methods have been developed to solve the leak detection problem, some drawbacks such as continuous monitoring and robustness should be addressed yet. Thus, this paper presents the main results of using a leaks detection and classification methodology, which takes advantage of piezodiagnostics principle. It consists of: i) transmitting/sensing guided waves along the pipe surface by means of piezoelectric device ii) representing statistically the cross-correlated piezoelectric measurements by using Principal Component Analysis iii) identifying leaks by using error indexes computed from a statistical baseline model and iv) verifying the performance of the methodology by using a Self-Organizing Map as visualization tool and considering different leak scenario. In this sense, the methodology was experimentally evaluated in a carbon-steel pipe loop under different leaks scenarios, with several sizes and locations. In addition, the sensitivity of the methodology to temperature, humidity and pressure variations was experimentally validated. Therefore, the effectiveness of the methodology to detect and classify pipe leaks, under varying environmental and operational conditions, was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, and Self-Organizing Maps, become as promising solution in the field of structural health monitoring and specifically to achieve robust solution for pipe leak detection.

Design of a Novel In-Pipe Reliable Leak Detector

Leakage is the major factor for unaccounted losses in every pipe network around the world (oil, gas, or water). In most cases, the deleterious effects associated with the occurrence of leaks may present serious economical and health problems. Therefore, leaks must be quickly detected, located, and repaired. Unfortunately, most state-of-the-art leak detection systems have limited applicability, are neither reliable nor robust, while others depend on the user experience. In this paper, we present a new in-pipe leak detection system. It performs autonomous leak detection in pipes and, thus, eliminates the need for the user experience. This paper focuses on the detection module and its main characteristics. Detection in based on the presence of a pressure gradient in the neighborhood of the leak. Moreover, the proposed detector can sense leaks at any angle around the circumference of the pipe with only two sensors. We validate the concepts by building a prototype and evaluate the system's performance under real conditions in an experimental laboratory setup.

A New Internal Detection Method for Fluid Transportation Pipeline Leak Based on Active Electrolocation

Pipeline leak internal inspection method is a key technology of safety assessment for industrial pipeline. Weakly electric fish can sense its surrounding environment by generating electric fields and detecting distortions through their active electrolocation system. In this paper, a new internal inspection leak method for fluid transportation pipeline, the active electrolocation internal inspection method, is proposed to pipe detection which is inspired by active electrolocation system of weakly electric fish. An experimental system for fluid transportation pipe leak detection based on active electrolocation has been constructed. To test the feasibility of this method and this experimental system's performance, the detecting property of this method with different working conditions for different material pipe, metallic and non-metallic pipes, were studied in this paper. And then, FFT (fast Fourier transformation) and joint time-frequency analysis was applied to analyze and process experimental data. FFT provides an available method to analyze the experimental results in the frequency domain at different point. Meanwhile, the joint time-frequency analysis method can achieve the observation any frequency component in the whole time domain. Finally, the positioning error and relative error of this method has been discussed and analyzed in this paper. In our study, experimental results show that leak spot in seamless steel pipe and PVC (polyvinyl chloride polymer) pipe with different working conditions can be effectively detected by the method. The internal inspection method for fluid transportation pipeline leak based on active electrolocation was validated by our study, which lays a foundation of a new online detection in pipe engineering field. A new internal inspection method for fluid transportation pipeline based on active electrolocation has been developed.