AIN-Based MEMS (Micro-Electro-Mechanical System) Hydrophone Sensors for IoT Water Leakage Detection System

Phua, Wee Kee; Rabeek, S. Mohamed; Han, Bo Wen; Njihof, Edwin; Huang, Tyler Tianlu; Chai, Kevin Tshun Chuan; Yeo, Jason Hock Huat; Lim, Soon Thor

doi:10.3390/w12112966

Cited by 20 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, a high-sensitivity MEMS hydrophone with a sensitivity of −170 dB is proposed for pipeline monitoring and shows a significant improvement in sensitivity compared to hydrophones used for leak monitoring reported in the literature [ 22 , 23 ]. A 31 m pipeline leak monitoring platform is built to demonstrate the performance of the proposed MEMS hydrophone.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring

Zhi

Chen

et al. 2023

Micromachines

View full text Add to dashboard Cite

In this work, a miniaturized, low-cost, low-power and high-sensitivity AlN-based micro-electro-mechanical system (MEMS) hydrophone is proposed for monitoring water pipeline leaks. The proposed MEMS Hydrophone consists of a piezoelectric micromachined ultrasonic transducer (PMUT) array, an acoustic matching layer and a pre-amplifier amplifier circuit. The array has 4 (2 × 2) PMUT elements with a first-order resonant frequency of 41.58 kHz. Due to impedance matching of the acoustic matching layer and the 40 dB gain of the pre-amplifier amplifier circuit, the packaged MEMS Hydrophone has a high sound pressure sensitivity of −170 ± 2 dB (re: 1 V/μPa). The performance with respect to detecting pipeline leaks and locating leak points is demonstrated on a 31 m stainless leaking pipeline platform. The standard deviation (STD) of the hydroacoustic signal and Monitoring Index Efficiency (MIE) are extracted as features of the pipeline leak. A random forest model is trained for accurately classifying the leak and no-leak cases using the above features, and the accuracy of the model is about 97.69%. The cross-correlation method is used to locate the leak point, and the localization relative error is about 10.84% for a small leak of 12 L/min.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The MEMS hydrophone was able to capture the leak signal and locate its position. Phua et al proposed a smart hydrophone sensing network combining MEMS hydrophones and the Internet of Things (IoT) [ 23 ]. By analyzing the transient signals captured by the MEMS hydrophone, critical leak acoustic information could be obtained.…”

Section: Introductionmentioning

confidence: 99%

A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring

Zhi

Chen

et al. 2023

Micromachines

View full text Add to dashboard Cite

show abstract

“…Sound signals are recorded using a data logger or PC software. Guo et al, 2022;Phua et al, 2020) Ground microphone An acoustic listening device that amplifies the sound signal can be used for pinpointing the leaks. (Quintiliani et al, 2020) Flowmeter Electro-magnetic or ultrasonic flowmeters quantify the flow of fluid passing through the pipe.…”

Section: Sensing System Working Principle Examplesmentioning

confidence: 99%

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

Bakhtawar

Zayed

2023

WIREs Water

View full text Add to dashboard Cite

Effective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water > Methods Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

show abstract

“…Sensing functionality in IoT serves to collect data through the IoT WSN node. usually sensing are done using direct measurement such as humidity sensor [140], [141], temperature sensor [142]- [144], gas sensor [145]- [147], piezoelectric [148], and many other [149]- [151]. However there are several indirect sensing such as the use of GPS to measure distance traveled [152], speed [153], tracking current location [154], [155], and also a camera for video [156] or picture sensing [157]- [159].…”

Section: B Sensingmentioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

View full text Add to dashboard Cite

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

show abstract

AIN-Based MEMS (Micro-Electro-Mechanical System) Hydrophone Sensors for IoT Water Leakage Detection System

Cited by 20 publications

References 22 publications

A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring

A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Contact Info

Product

Resources

About