Transactions of the Korean Society of Mechanical Engineers B (대한기계학회논문집B)

The Korean Society of Mechanical Engineers (대한기계학회)
권호 표지
DOI QR코드

DOI QR Code

A Monitoring System Based on an Artificial Neural Network for Real-Time Diagnosis on Operating Status of Piping System

가스배관망 작동상태 실시간 진단용 인공신경망 기반 모니터링 시스템

  • Jeon, Min Gyu (Gas Solution Center, Korea Maritime Univ.) ;
  • Cho, Gyong Rae (Division of Mechanical and Information Engineering, Korea Maritime Univ.) ;
  • Lee, Kang Ki (Department of Offshore Plant Managements, Korea Maritime Univ.) ;
  • Doh, Deog Hee (Division of Mechanical and Information Engineering, Korea Maritime Univ.)
  • 전민규 (한국해양대학교 가스솔루션센터) ;
  • 조경래 (한국해양대학교 기계에너지시스템공학부) ;
  • 이강기 (한국해양대학교 해양플랜트운영학과) ;
  • 도덕희 (한국해양대학교 기계에너지시스템공학부)
  • Received : 2014.09.09
  • Accepted : 2014.10.20
  • Published : 2015.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a new diagnosis method which can predict the working states of a pipe or its element in realtime is proposed by using an artificial neural network. The displacement data of an inspection element of a piping system are obtained by the use of PIV (particle image velocimetry), and are used for teaching a neural network. The measurement system consists of a camera, a light source and a host computer in which the artificial neural network is installed. In order to validate the constructed monitoring system, performance test was attempted for two kinds of mobile phone of which vibration modes are known. Three values of acceleration (minimum, maximum, mean) were tested for teaching the neural network. It was verified that mean values were appropriate to be used for monitoring data. The constructed diagnosis system could monitor the operation condition of a gas pipe.

본 연구에서는 인공신경망을 이용하여 배관이나 배관요소의 작동상태를 예측할 수 있는 진단방법을 제안한다. 입자영상유속계 기술을 이용하여 얻어진 배관의 검사부위의 진동에 의한 이동량을 인공신경망의 학습용으로 사용한다. 측정시스템은 카메라, 조명, 인공신경망이 탑재된 호스트컴퓨터로 구성된다. 구축된 모니터링시스템이 제대로 작동하는지 이미 알고 있는 진동원(2개의 휴대폰)에 대하여 적용하였다. 진동가속도의 최소값, 최대값, 평균값을 인공신경망의 학습에 사용해 본 결과, 평균값이 진동상태의 실시간 모니터링에 적합함을 확인하였다. 구축된 진단시스템은 실제 가스배관의 작동상태에 대하여 모니터링 가능함이 확인되었다.

Keywords

References

  1. Adrian, R. J., 1991, "Particle-Imaging Techniques for Experimental Fluid Mechanics," Ann. Rev. J. Fluid Mech., Vol.23, pp.261-304. https://doi.org/10.1146/annurev.fl.23.010191.001401
  2. Machida, K., Okamura, H., Hirano, T. and Usui, K., 2001, "Stress Analysis of Mixed-Mode Crack of Homogeneous and Dissimilar Materials by Speckle Photography," Transactions of the Japan Society of Material Engineers, Vol. 67, No. 655, pp.86-91. https://doi.org/10.1299/kikaia.67.86
  3. Braun, S., 1975, "The Extraction of Periodic Waveforms by Time Domain Averaging," Acustica, Vol. 32, No. 2, pp.69-77.
  4. Shin, K. H., "Realization of the Real-Time Domain Averaging Method using the Kalman Filter," International Journal of Precision Eng. and Manufacturing, Vol. 12, No. 3, pp.413-418. https://doi.org/10.1007/s12541-011-0053-4
  5. Jeon, M. G., Cho, G. R., Oh, J. S., Lee, C. J. and Doh, D. H., 2013, "Measurements of Remote Micro Displacements of the Piping System and a Real Time Diagnosis on Their Working States Using a PIV and a Neural Network," Trans. of the Korean Hydrogen and New Energy Society, Vol. 24, No. 3, pp. 264-274. https://doi.org/10.7316/KHNES.2013.24.3.264
  6. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986, "Learning representations by back-propagating errors," Nature, pp. 323-333.
  7. Utami, T. and Blackwelder, R. A., 1991, "A Cross Correlation Technique for Velocity Field Extraction from Particulate Visualization," Exp. in Fluids, Vol.10, pp.213-223. https://doi.org/10.1007/BF00190391
  8. Kimura, I., Takamori, T. and Inoue, T., 1986, "Image Processing Instrumentation of Flow by using Correlation Technique," Journal of Flow Visualization and Image Processing, Vol. 6, No.22, pp.105-108.
  9. Nobach, H. and Bodenschatz, E., 2009, "Limitations of Accuracy in PIV due to Individual Variations of Particle Image Intensities," Exp. in Fluids, Vol. 47, pp. 27-38. https://doi.org/10.1007/s00348-009-0627-4