Abstract
There is an urgent need for industrial Internet of things (IoT) solutions to deploy a smart hydrophone sensor grid to monitor pipeline health and to provide an accurate prediction in the event of any leakage. One solution is to develop an IoT water leakage detection system consisting of an interface to capture acoustic signals from aluminum nitride (AlN)-based micro-machined infrasonic hydrophone sensors that are fed as inputs and predict an approximate leak location as a form of output. Micro-electro-mechanical systems (MEMS) are particularly useful for IoT applications with low power consumption and small device footprint. Data analytics including characterization, pre/post processing are applied to determine the leaks. In this work, we have developed the process flow and algorithm to detect pipe leakage occurrence and pinpoint the location accurately. Our approach can be implemented to detect leaks for different pipe lengths, diameters and materials.
Highlights
Water is one of most important natural resources, the availability of which directly impacts the livelihood and survival of humankind
The mechanical systems (MEMS) hydrophone used in this study is a piezoelectric aluminum nitride (AlN)-based micro-machined infrasonic hydrophone sensor
The benefits of adopting the MEMS hydrophone far outweighs those of other existing hydrophones in that it allows for higher optimal sensitivity, small footprint, lower cost and easier scalability for large-scale Internet of things (IoT) implementation
Summary
Water is one of most important natural resources, the availability of which directly impacts the livelihood and survival of humankind. There are many ways to detect water leakages in pipelines [3,4,5,6] Almost all of these monitoring techniques make use of onsite measurements which can be invasive or non-invasive. The proposed water leakage detection system in this work will illustrate to the industry that it is possible to isolate the leak location, showcase the potential to scale up and demonstrate deployment feasibility in a multigrid sensor environment. This technology and derivative solutions will become an important building block to a smart nation with industry 4.0-ready pipeline systems in smart factories and smart estates. We were able to establish leakage detection in a sensor network with allowance for automatic detection and real-time monitoring, prompting alerts only with the onset of abnormal events
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