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

  • 제24권9호
  • /
  • Pages.21-27
  • /
  • 2019
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

Unsupervised Learning-Based Pipe Leak Detection using Deep Auto-Encoder

  • 투고 : 2019.08.14
  • 심사 : 2019.09.04
  • 발행 : 2019.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

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.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Parallel Distributed Processing. Vol. 1: Foundations," MIT Press, pp. 318-362, 1986.
  4. F. Rosenblatt, "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms," Spartan Books, 1961.
  5. 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
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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
  11. K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation," arXiv preprint arXiv:1406.1078, 2014.
  12. 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.
  13. 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.
  14. 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.
  15. S. Ioffe and C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariant shift, Proceedings of 32nd International Conference on Machine Learning (ICML), pp. 1-9, Lille, France, Jul. 2015.