Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

DEVELOPMENT OF AN AUTOMATED GAS-LEAKAGE MONITORING SYSTEM WITH FEEDBACK AND FEEDFORWARD CONTROL BY UTILIZING IOT

Liquefied Petroleum Gas (LPG) is used in many ranges of applications like home and industrial appliances, in vehicles and as a propellant and refrigerator. However, leakage of LPG produces hazardous and toxic impact on human begins and other living creatures. There by, the authors developed a system to monitor the LPG gas leakage and make alert to users of it. In this research, MQ-6 gas sensor is used for sensing the level of gas concentration of a closed volume; and to monitor the consequences of environmental changes an IoT platform has been introduced. Robust control along with cloud based manual control has been applied so that the gas leakage can be prevented in the response of either feedback or feedforward commands individually. It switches on the specified relays to control the level of gas concentration in the time of leakage the excess gas in times of leakage. It rechecks the value again and again if it crosses 300 ppm it will setup a relay-based switching on control mechanism using Thingspeak cloud. The controller used here is Node-MCU v:1.0. This research provides design approach on both software and hardware. Hence an embedded system comprising of Relay switches, Embedded C++, Gas sensor, Temperature & Humidity sensor along with Internet of Things (IoT) is fabricated to meet the objectives of the current research

Cyber-Physical Systems for Smart Water Networks: A Review

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.acceptedVersio

Gas Pressure Measurement Device and Medical Vacuum Design

Two methods are used in a digital pressure meter available in the market, namely positive pressure, and negative pressure. The positive one is used to measure the air pressure on a sphygmomanometer and medical gas pressure at an outlet in the treatment room so that operators can easily check medical gas pressure for the safety of the patient, or they can routinely check to find out how much pressure of the medical gas. Based on the background, the research aimed to design a digital pressure meter equipped with a medical gas measurement mode so that the device can be used to calibrate the sphygmomanometer, suction pump, and measure the medical gas pressure available at the medical gas outlet in each treatment room

Application of software and hardware-based technologies in leaks and burst detection in water pipe networks: a literature review

With the rise of smart water cities, water resource management has become increasingly important. The increase in the use of intelligent leak detection technologies in the water, gas, oil, and chemical industries has led to a significant improvement in safety, customer, and environmental results, and management costs. The aim of this review article is to provide a comprehensive overview of the application of software and hardware-based technologies in leak detection and bursts in water pipeline networks. This review aims to investigate the existing literature on the subject and to analyse the key leak detection systems in the water industry. The novelty of this review is the comprehensive analysis of the literature on software and hardware-based technologies for leak and burst detection in water pipe networks. Overall, this review article contributes to understanding the latest developments and challenges in the application of software- and hardware-based technologies for leak and burst detection in water pipe networks, and serves as a valuable resource for researchers, engineers, and practitioners working in the field of water distribution systems