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Conference Paper A study on Improvement of Resource Efficiency for IoT-based Pipe Leak Detection
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Authors
Se Won Oh, Ji-Hoon Bae, Doo-Byung Yoon, Bong-su Yang, Gwan Joong Kim, Hyeon Soo Kim
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1423-1425
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539385
Abstract
In managing today's complex and aging power plant facilities safely, increasing attention has been paid to the challenges of detecting pipe leak faults quickly and accurately. This study focuses on developing a resource-efficient leak detection system using distributed acoustic sensors, in pursuit of the Internet of Things (IoT) paradigm. The proposed system extracts a small number of featured predictors from the raw acoustic signals, so the presence of leaks can be readily detected by applying machine learning classifiers while reducing the burden on data transmission, storage and computation. A system prototype is successfully evaluated through the experiments with acoustic signals measured around a laboratory scale nuclear power plant coolant pipelines, considering the ambient background and machinery noises.
KSP Keywords
Acoustic Sensor, Acoustic signal, Data transmission, Internet of thing(IoT), Intrusion detection system(IDS), IoT-based, Laboratory scale, Leak detection system, Machine learning classifiers, Nuclear Power Plant(NPP), Resource efficiency