Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Leakage Detection Method in Water Pipe using Tree-based Boosting Algorithm

트리 기반 부스팅 알고리듬을 이용한 상수도관 누수 탐지 방법

  • Jae-Heung Lee (Department of Computer Engineering, Hanbat National University) ;
  • Yunsung Oh (Department of Computer Engineering, Hanbat National University) ;
  • Junhyeok Min (Department of Computer Engineering, Hanbat National University)
  • 이재흥 (한밭대학교 컴퓨터공학과) ;
  • 오윤성 (한밭대학교 컴퓨터공학과) ;
  • 민준혁 (한밭대학교 컴퓨터공학과)
  • Received : 2024.03.04
  • Accepted : 2024.03.29
  • Published : 2024.04.30
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Abstract

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

국내 상수도관의 파열, 결함 등으로 인한 누수율로 인한 손실이 매우 크고, 이런 누수를 예방을 위한 방지 대책이 필요한 상황이다. 본 논문에서는 진동 센서를 활용한 누수 탐지 센서를 개발하고 인공지능 기술을 활용한 최적의 누수 탐지 알고리듬을 제시하고자 한다. 상수도 배관에서 취득한 진동음은 FFT(Fast Fourier Transform)를 이용한 전처리 과정을 거친 뒤, 최적화된 트리 기반 부스팅 알고리듬을 적용하여 누수 분류를 하였다. 다양한 실증 환경에서 취득한 약 26만여 개의 실험 데이터에 적용한 결과 기존의 SVM(Support Vector Machine) 방법에 비해약 4%가 향상된 97%의 정확도를 얻었고, 연산 처리속도는 약 1,362배가 향상되어 엣지 디바이스 적용에도 적합함을 확인하였다.

Keywords

Acknowledgement

본논문은 산업통상자원부 및 한국산업기술기획평가원(KEIT) 연구비 지원에 의한 연구임.

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