Leak Detection System Research Articles

Perancangan Alat Sensor Pendeteksi Kebocoran Gas LPG dan Api dengan Menggunakan MQ-2 Dan Flame Sensor

LPG gas leaks can become a serious threat to human safety and health, especially if they are not detected quickly and accurately in the main control system for this LPG gas leak detection system and even if the worst possibilities occur, such as a spark which could result in a light fire. This research uses the Prototype method. Another goal is to minimize the risk of accidents when using LPG gas stoves, and reduce the risk of damage and material losses arising from gas leaks and fires caused by LPG gas stoves. Design and manufacture of hardware and software by developing the MQ-2 sensor detection algorithm which is used to detect gas leaks and Flame sensors for fire detection. Testing is carried out by simulating various test scenarios from the use of gas sensors and fire sensors. The test results show that this system is capable of detecting detected gas and fire. Tests are carried out alternately between gas sensors and fire sensors. The output on the system is in the form of SMS notifications, Bazzer and LED lights. SMS notification using SIM800L. When a fire is detected there will be an SMS notification "API DETECTED!!!", the bazzer will sound and the red LED will light up. When gas is detected there will be an SMS notification "GAS DETECTED!!!!" and the yellow LED will light up, the maximum limit if gas is detected is 10cm and the maximum limit if fire is detected is 30 cm. In conclusion, the tool is made using the Arduino Nano microcontroller, by making a cable design on the PCB board, after which the soldering process or connecting the modules is carried out.

Relative humidity prediction of a leakage area for small RCS leakage quantification by applying the Bi-LSTM neural networks

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

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.

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.

Arduino-Based Safety System for Gas Flame Monitoring, Leakage Detection, and Utensil Presence Using Sensor Integration and SMS Alerts

Abstract: In this research project, we present an innovative Arduino-based safety system designed to enhance household gas safety by monitoring gas flame, detecting gas leakage, and ensuring the presence of utensils in the cooking area. By integrating various sensors such as a flame sensor, gas leakage sensor, and ultrasonic sensor, coupled with servo motor control and GSM technology, our system provides real-time monitoring and proactive response to potential hazards. This project showcases the integration of hardware components and intelligent control algorithms, enabling an efficient and reliable safety system. The use of Arduino as the central control unit provides a flexible and cost-effective solution for gas safety applications. Experimental results demonstrate the effectiveness of our system in detecting potential hazards, triggering appropriate actions, and notifying users via SMS alerts.

Machine learning-based system for vacuum leakage detection in EAST fusion device

In order to achieve real-time monitoring of partial pressures of residual gases and timely detection of vacuum leakage in fusion devices, a machine learning-based system was successfully developed on the LabVIEW software platform for its first-time application in the EAST fusion device. This system acquires partial pressure data from the quadrupole mass spectrometer (QMS) in both histogram and P&T scanning modes to identify the nature of vacuum leakage. The backpropagation (BP) neural network algorithm is employed for leakage detection. To train the BP neural network model, historical data from normal and leakage scenarios on the EAST device are utilized as inputs. Subsequently, the trained model is integrated into the system. The BP neural network model exhibited an identification accuracy of 95.27 % on the test set, effectively detecting water and gas leakage on EAST. Consequently, this system enables rapid detection of vacuum leakage in fusion devices, facilitating prompt mitigation of their impact on plasma experiments and ensuring the device's safety and stability.

Study on the leakage morphology and temperature variations in the soil zone during large-scale buried CO2 pipeline leakage

In the CCUS technology chain, buried CO2 pipelines are inevitable under special terrain conditions. Due to the concealment and the complexity of the soil, they have a higher possibility of leakage. However, there is limited research on buried CO2 pipeline leakage processes. In this study, authors used existing industrial-scale pipelines as CO2 storage containers and constructed a large-scale buried CO2 pipeline experimental system. The leakage hole size and direction were considered to investigate the leakage morphology and temperature changes in the soil. The results showed that during the small-hole leakage, dry ice spheres were formed, which adhered to the pipeline. According to the change of temperature field, the dry ice spheres expanded towards weak areas in the soil, rather than strictly along the jet direction. The volume of dry ice spheres generated in the upward leakage process was the largest, while the volume was the smallest in the downward leakage process. Due to the soil resistance, the volume of dry ice spheres generated in the 3 mm leakage process was always greater than 9 times that of the 1 mm leakage process. The experimental results provide important references for optimizing leakage detection systems and conducting leakage risk assessments.

An efficient system for water leak detection and localization based on IoT and lightweight deep learning

Nowadays, Water Distribution Networks (WDNs) efficiency is limited due to leakage issues, which result in large water losses that exceed about one third of the input volume. Considering the problems of growing population and water scarcity especially in Africa and Asia continents, an urgent humanitarian need arisen to efficiently detect and localize pipeline water leaks. To address this need, this paper proposes an end-to-end system for leak detection and localization in water pipelines based on IoT and deep learning. The IoT solution is deployed allowing real time monitoring of pipelines by using non-invasive IoT devices equipped with acoustic sensors which are installed on the pipeline surface. A lightweight one-Dimensional Convolutional Neural Network (1D-CNN) classification model is embedded on these devices to detect leaks. This model uses raw audio signals generated by sensors as input information. Information about the detected leaks is then remotely transmitted using long range and low energy LoRaWAN protocol to the LoRa gateway. The gateway relays messages to a data processing Server. A 1D-CNN regression model has been then created and deployed in this server to directly estimate the 2D position of the leak. As an illustration, An IoT device prototype is designed and used as a case study to test the performance of the proposed system. We demonstrated that our proposal achieves an accuracy of 97 % for detecting leaks and can fix the 2D position of leak with an error of 0.0006 on the X coordinate and 0.0004 on the Y coordinate. Experiments confirm the effectiveness of the proposed solution.

Precision Gas Leakage Detection System with Integrated Sensors

Gas leaks pose significant safety risks in both residential and industrial settings, making gas leakage detectors essential safety devices for many years. These detectors serve the crucial function of identifying the presence of hazardous gases, such as carbon monoxide, and activating an alarm system to promptly alert occupants of the building. This innovative gas leak detection system leverages mobile technology, integrating phone calls, SMS, WhatsApp, and GPS location services. Additionally, it features a unique capability to shut off the power supply in the affected area, providing a real-time and effective means of gas leak detection and containment. This gas leakage detection system prototype has been successfully developed and tested with gases such as BUTANE and PROPENE. The experimental results demonstrate that the system can detect gas leaks in less than a minute. The primary objective of this gas detection project is to implement a security system for identifying gas leaks in closed environments. It utilizes MQ-2 sensors designed to function effectively within enclosed spaces. Furthermore, this system can be seamlessly integrated with other security and safety systems for enhanced overall protection.

Automatic LPG Leakage Detector using Arduino (UNO)

Gas leakage is a major problem in industries, residential premises and gas powered vehicles. The leakage if not detected may lead to explosion and cause severe damages to life and environment. The conventional leakage detection system uses on-site alarms for warning. The "LPG Gas Leakage Detector Using Arduino" is an innovative safety system designed to detect and respond to the presence of Liquefied Petroleum Gas (LPG) leaks in various environments, including homes, kitchens, and industrial settings. This device leverages Arduino microcontroller technology and gas sensors to provide real-time monitoring and alerts. The system offers an additional layer of protection against the potential hazards and dangers associated with gas leaks.

Pipeline Leak Detection System

This article deals with a development of a pipeline leak detection system for monitoring hydrocarbon pipelines in real time. This system is based on the integration of methods of negative pressure waves of propagation and mass and momentum balance calculation. The system is designed to receive real-time data from multiple remote terminals which operate in the field and its processing in a monitoring center. For validation of the system, a liquefied gas pipeline of 20” diameter and 60 km in length was used. The validation of the system was done by comparing the events detected and located by the developed system and the results of verification of real leak locations. Some inspection results using this system are presented.

Design Of An LPG Leak Detection System Using Iot Based MQ-2 Sensor

The IoT-based LPG gas leak detection system with MQ-2 and automatic regulator aperture is designed to protect the environment from the dangers of gas leaks and optimize gas use. The MQ-2 sensor is used to detect LPG gas accurately and sensitively. This system is connected to IoT which allows remote monitoring via smart devices. When the sensor detects that the LPG gas concentration exceeds a safe threshold, the system will send an alert with notification and automatically activate the regulator to cut off the gas supply. This helps prevent the accumulation of harmful gases. This design combines reliable gas detection with automatic operation to improve environmental safety and gas efficiency. LPG gas is very commonly used by the community because it has many advantages, but there are also many risks associated with using LPG gas, such as poisoning, shortness of breath and even fire. It is therefore important to have a leak detection system to prevent accidents that may occur, by integrating the programmable MQ-2 sensor and NoteMCU ESP8266.

A LoRaWAN-based IoT System for Leakage Detection in Pipelines

Leakages in a pipeline are an important problem due to the potential economic and environmental hazard they present. In this study, we proposed a LoRaWAN-based approach for detecting and localizing leakages in pipelines. Our study includes an experimental setup that simulates a pipeline network with pressure and flow rate sensors attached. The flow rate and pressure data were transmitted through LoRaWAN to a receiver, which in turn uploads the data to a cloud server using a cellular network. The receiver compares the flow rate reading from all the monitoring nodes attached to the pipeline network. If flow rate reading from successive nodes presents a percentage variation of more than 1.5%, a leak is confirmed to have taken place. The flow rate readings can also be used to localize the leak. The resolution of the leak detection is dependent on the number of monitoring nodes on the pipeline network. In our study, the pressure readings were found to be insufficient to provide reliable evidence of leakages. In our specific situation, due to the relatively short length of the experimental pipeline network, a pressure drop of up to 38.2% was recorded between successive nodes with an overall pressure loss of 62%, making pressure data unsuitable for leak detection in the short pipeline network.

Computer Vision Based Leakage Detection System Dedicated for Use in Aviation Component Test Laboratories

Computer Vision Based Leakage Detection System Dedicated for Use in Aviation Component Test Laboratories

Standalone power system with photovoltaic and thermoelectric installations for power supply of remote monitoring and control stations for oil pipelines

During the operation of oil pipelines, accidents with oil spills may occur, which can cause severe damage to the ecological situation in the region and disrupt many processes in nature. The oil pipeline’s remote monitoring and control systems (RMCS) are used to prevent such situations. In areas remote from the centralized power supply, it is necessary to use a standalone power system (SPS) to provide electricity to RMCS facilities. Examples of such facilities are remote monitoring and control (RMC) stations, including systems for monitoring the state of the oil pipeline and its parameters, leak detection systems, and the pipeline’s cathodic protection. The paper proposes a methodology for sizing SPS with a photovoltaic plant, an on-pipe thermoelectric generator, and batteries for the power supply of the RMC station. The idea of the work is to use energy sources that are most efficient at opposite seasons of the year. Particle swarm optimization is used to optimize the sizing of SPS components. The technical optimization criterion is the loss of load probability. The economic optimization criterion is the levelized cost of electricity. The impact of the oil temperature at the point of the RMC station on the efficiency of the thermoelectric generator is estimated. The calculations carried out in this work were made for the climate region of the Vankorsk oil and gas field in the north of Russia. The methodology proposed in this paper can also be used for northern territories, such as Canada, Norway, and the USA.

Defect Size Quantification for Pipeline Magnetic Flux Leakage Detection System via Multilevel Knowledge-Guided Neural Network

Defect size quantification plays a vital role in pipeline magnetic flux leakage (MFL) detection system. However, most existing methods suffer from poor applicability and low precision due to complex industrial process. Moreover, they often overlooks the valuable knowledge that may be beneficial for defect size quantification. To solve this problem, a defect size quantification method based on multi-level knowledge-guided neural network (MLKG-NN) is proposed, which effectively integrates prior knowledge and specific data. First, at the feature level, a recursive residual subnet is proposed to inject the mechanism features into the network, so that the network performance can be enhanced. Second, an experience-aided subnet is proposed to incorporate expert experience at the decision level, which supervises the network training with labels, so that the network stability can be improved. Third, at the modeling level, a cascade expression subnet based on two-point representation is first proposed in the defect size quantification area, where both the value and distribution of labels are considered to boost the network precision. These three parts are jointly trained and promote each other. Finally, extensive experiments are conducted with defects from a pipeline network in northern China and the experimental results highlight the superiority of our method.

High-Precision Single-Leak Detection and Localization in Single-Phase Liquid Pipelines Using the Negative Pressure Wave Technique: An Application in a Real-Field Case Study

Summary This paper presents a novel workflow for high-precision leak detection in pipeline networks using the negative pressure wave (NPW) technique. The proposed workflow addresses challenges associated with noisy and convoluted pressure transducer data, rapid pressure decay, and the need for robustness in leak event detection. To overcome these challenges, the workflow incorporates data preprocessing techniques for cleansing, normalization, and denoising, as well as dynamic pressure control limit lines to differentiate between pump and leak events. Multiple transducer analysis techniques are used to minimize false positives. Synthetic leak scenarios are generated using the Water Network Tool for Resilience (WNTR) package, enabling a comprehensive assessment of the workflow’s performance. The generated scenarios are validated through pressure history matching against field inline pressure recordings. A dashboard is developed for real-time visualization and verification of leak events. The effectiveness of the workflow is demonstrated through testing on a real network, resulting in the successful detection and precise localization of a confirmed leak event. The workflow proves its capability to achieve high accuracy, with a 100-m resolution in a complex network configuration with 29 pipe sections and 1-Hz pressure signal recordings. For synthetic leak events, a 10-Hz pressure signal is utilized, achieving a remarkable 10-m accuracy. Moreover, the integration of the workflow with supervisory control and data acquisition (SCADA) systems is showcased, highlighting its potential for near real-time leak detection in practical applications. Overall, this paper presents a comprehensive and effective workflow for high-precision leak detection and localization in pipeline networks, offering valuable insights into improving the efficiency and reliability of leak detection systems.

A Novel Technique for Detecting Underground Water Pipeline Leakage Using the Internet of Things

Water-pipeline leakage is one of the most common problems that depletes water supplies. Countries like Jordan, which are really experiencing a water deficit, are particularly concerned about this issue. The lack of monitoring tools makes the underground water-pipeline leakage a challenge since the pipelines are invisible. Besides, reducing the amount of time needed to precisely detect and locate the leak is another challenge. If not reduced, the aforementioned element has an effect on cost. A small broken water distribution line costs $64,000 per year. In Jordan, water leakage costs $1.7 million. This expense can be significantly decreased using an effective early water leak detection system. In this paper, we proposed an efficient internet of things system for detecting water-pipeline leakage based on a shielded pipeline, a NodeMCU, a soil moisture sensor, and the Firebase database. We created a baseline system, and then we tested and evaluated the proposed system when various types of soil are used. Furthermore, this paper compared several strategies offered for detecting water-pipeline leaking including the proposed system. The results showed that the proposed system reduced the time required for detecting water-pipeline leakage by 70% and the system hardware cost by 83% compared with the earlier work. It was difficult to compare the total cost of the proposed system with the total cost of previous works since the total cost is not calculated in their systems.  Besides, in this paper, we proposed an IoT system for securing the underground water pipelines from adversaries.

LoRaWAN-based Petroleum Pipeline Leakage Detection System Using Pressure Profile under a Pump Proximity Effect Condition

Leakages in pipeline is an important problem that can occur at any stage of the pipeline lifespan due to ageing, improper installation, or human related factors like bunkering or vandalization. Several invasive and non-invasive techniques are being used which have proven to be successful. However, this study focuses on leakage in an area of the pipeline network that is most often overlooked. This area is within the first 100 meters of the pipeline network to the excitation pump and is often prone to leakages due to high pressure. This study shows that even at no leak conditions pressure profile of a short pipeline in a closed loop configuration can vary by as much 59.93 percent. The study also examined the impact of single leak, double leaks, and triple leaks on the pipeline network with pressure loss compared to the no leak condition ranging from about 10 percent to as high as 60 percent.

Fault detection using PDE-based observer in transport flow

This paper deals with the state fault detection scheme for distribution flow networks subject to continuously varying conditions at boundaries. A robust PDE detection observer for transport flow systems is designed. Directly built on the nonlinear hyperbolic systems of balance laws model with anti-collocated setup, the PDE observer based on backstepping theory provide the on-line estimation of signals that are not measured. The stability of the error equation is proved. The estimation and the observability time are used for fault detection; an adaptive threshold is defined for the purpose. The performances of the observer and the fault detection method are validated on actual flow data collected from a real water distribution system (WDS) for leakage detection. The leak detection time corresponds to the first alarm activation, confirms the effectiveness of proposed approach.