한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제25권2호
  • /
  • Pages.59-66
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  • 2020
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Pipe Leak Detection System using Wireless Acoustic Sensor Module and Deep Auto-Encoder

  • Yeo, Doyeob (Electronics and Telecommunications Research Institute (ETRI)) ;
  • Lee, Giyoung (Electronics and Telecommunications Research Institute (ETRI)) ;
  • Lee, Jae-Cheol (Korea Atomic Energy Research Institute (KAERI))
  • 투고 : 2019.12.13
  • 심사 : 2020.01.17
  • 발행 : 2020.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 저전력 무선 음향센서 모듈을 통한 데이터 수집과 딥 오토인코더를 이용한 데이터 분석을 통해 배관의 누출을 탐지하는 시스템을 제안한다. 데이터 통신량을 줄이기 위해서 푸리에 변환을 통해 음향센서 데이터 양을 약 1/800로 감소시키는 저전력 무선 음향센서 모듈을 구성하였고, 20kHz~100kHz 주파수 신호를 이용하여 가청 주파수 대역에서 발생하는 노이즈에 강인한 누출 탐지 시스템을 설계하였다. 또한, 데이터 양의 감소에도 배관 누출을 정확하게 탐지하도록 딥 오토인코더를 이용한 데이터 분석 기법을 설계하였다. 수치적인 실험을 통해, 본 논문에서 제안한 누출 탐지 시스템이 고주파 대역대의 노이즈가 섞인 환경에서도 99.94%의 높은 정확도와 Type-II error 0%의 높은 성능을 보이는 것을 검증하였다.

In this paper, we propose a pipe leak detection system through data collection using low-power wireless acoustic sensor modules and data analysis using deep auto-encoder. Based on the Fourier transform, we propose a low-power wireless acoustic sensor module that reduces data traffic by reducing the amount of acoustic sensor data to about 1/800, and we design the system that is robust to noise generated in the audible frequency band using only 20kHz~100kHz frequency signals. In addition, the proposed system is designed using a deep auto-encoder to accurately detect pipe leaks even with a reduced amount of data. Numerical experiments show that the proposed pipe leak detection system has a high accuracy of 99.94% and Type-II error of 0% even in the environment where high frequency band noise is mixed.

키워드

참고문헌

  1. D.-B. Yoon, S.-S. Moon, and B.-S. Yang, A Study on Acoustic Signal Processing Method for Detecting Small Leak of Piping System, Proceedings of the Domestic conference on the Korean Society for Noise and Vibration Engineering, pp. 139-139, Hoengseong, Korea, Oct. 2016.
  2. J.-H. Bae, D. Yeo, D.-B. Yoon, S.W. Oh, G.J. Kim, N.S. Kim, and C.S. Pyo, Deep-Learning-Based Pipe Leak Detection Using Image-Based Leak Features, Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 2361-2365, Athens, Greece, Oct. 2018.
  3. D. Yeo, J.-H. Bae, and J.-C. Lee, “Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder,” Journal of The Korea Society of Computer and Information, Vol. 24, No. 9, pp. 21-27, September 2019. https://doi.org/10.9708/jksci.2019.24.09.021
  4. G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals, and Systems, Vol. 2, No. 4, pp. 303-314, 1989. https://doi.org/10.1007/BF02551274
  5. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going Deeper with Convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, Boston, USA, Jun. 2015.
  6. K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-scale Image Recognition, Proceedings of 5th International Conference on Learning Representations (ICLR), pp. 1-14, San Diego, USA, May 2015.
  7. K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-12, Las Vegas, USA, Jun. 2016.
  8. G. Huang, Z. Liu, L.V.D. Maaten, and K. Weinberger, Densely Connected Convolutional Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, Hawaii, USA, Jul. 2017.
  9. S. Hpchreiter and J. Schmidhuber, "Long short-term memory," Neural computation, Vol. 9, No. 8, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  10. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, "Playing atari with deep reinforcement learning," arXiv preprint arXiv:1312.5602, 2013.
  11. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lilicrap, T. Harley, and K. Kavukcuoglu, "Asynchronous Methods for Deep Reinforcement Learning," arXiv preprint arXiv:1602.01783, 2016.
  12. R. Chalapathy and S. Chawla, "Deep Learning for Anomaly Detection: A survey," arXiv preprint arXiv:1901:03407, 2019.
  13. A. Borghesi, A. Bartolini, M. Lombardi, M. Milano, and L. Benini, Anomaly detection using autoencoders in high performance computing systems, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, pp. 9428-9433, Hawaii, USA, Jul. 2019.
  14. T. Luo and S.G. Nagarajan, Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT, Proceedings of 2018 IEEE International Conference on Communications (ICC), pp. 1-6, Kansas City, USA, May 2018.
  15. J. Pereira and M. Silveira, Unsupervised Anomaly Detection in Energy Time Series Data using Variational Recurrent Autoencoders with Attention, Proceedings of 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1275-1282, Orlando, USA, Dec. 2018.
  16. ASTM E1002-11(2018), "Standard Practice for Leaks Using Ultrasonics," American Society for Testing and Materials (ASTM) International, West Conshohocken, PA, 2018, https://doi.org/10.1520/E1002-11R18, www.astm.org.
  17. NDIS 3420-2000: "Methods for Leak Test using Ultrasonics," Non-Destructive Inspection Society.
  18. Y. Pei, The gas leak locating detection based on the improved ultrasonic transducer array group, Proceedings of the International Conference on Machinery, Materials Science and Engineering Application (MMSE), pp. 251-255, Wuhan, China, June, 2015.