- Volume 49 Issue 7
DOI QR Code
Markov chain-based mass estimation method for loose part monitoring system and its performance
- Shin, Sung-Hwan (Department of Automotive Engineering, Kookmin University) ;
- Park, Jin-Ho (Advanced Condition Monitoring and Diagnostics Lab., Korea Atomic Energy Research Institute (KAERI)) ;
- Yoon, Doo-Byung (Advanced Condition Monitoring and Diagnostics Lab., Korea Atomic Energy Research Institute (KAERI)) ;
- Han, Soon-Woo (Advanced Condition Monitoring and Diagnostics Lab., Korea Atomic Energy Research Institute (KAERI)) ;
- Kang, To (Advanced Condition Monitoring and Diagnostics Lab., Korea Atomic Energy Research Institute (KAERI))
- Received : 2017.01.11
- Accepted : 2017.05.05
- Published : 2017.10.25
A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.
Supported by : National Research Foundation of Korea (NRF)
- B.J. Olma, Source location and mass estimation in loose parts monitoring of LWR's, Prog. Nucl. Energy 15 (1985) 583-594. https://doi.org/10.1016/0149-1970(85)90086-1
- S.M. Ziola, M.R. Gorman, Source location in thin plates using cross-correlation, J. Acoust. Soc. Am. 90 (1991) 2551. https://doi.org/10.1121/1.402348
- J.-S. Kim, I.-K. Hwang, J.-T. Kim, J. Lyou, Development of automatic algorithm for localizing loose parts with a steam generator, Nucl. Eng. Des. 219 (2002) 269-276.
- J.-H. Park, Y.-H. Kim, Impact source localization on an elastic plate in a noisy environment, Meas. Sci. Technol. 17 (2006) 2757-2766. https://doi.org/10.1088/0957-0233/17/10/030
- C.W. Mayo, Loose-part mass and energy estimation, Prog. Nucl. Energy 34 (1999) 263-282. https://doi.org/10.1016/S0149-1970(98)00010-9
- B.J. Olma, Experience with identification of loose part by acoustic monitoring of primary system, Prog. Nucl. Energy 43 (2003) 225-232. https://doi.org/10.1016/S0149-1970(03)00032-5
- T. Tsunoda, T. Kato, K. Hirata, Y. Sekido, K. Sendai, M. Segawa, S. Yamatoku, T. Morioka, K. Sano, O. Tsuneoka, Studies on the loose part evaluation technique, Prog. Nucl. Energy 15 (1985) 569-576. https://doi.org/10.1016/0149-1970(85)90084-8
- S. Kim, Y.-G. Lee, M.J. Jhung, Y.-B. Kim, A development of mass estimation tools based on pre-operation test for the loose part monitoring system of OPR-1000, in: Transaction of the Korean Nuclear Society Spring Meeting, 2012, pp. 17-18.
- J. Korbicz, J.M. Koscielny, Z. Kowalczuk, W. Cholewa, Fault Diagnosis: Models, Artificial Intelligence, Application, Springer-Verlag, Berlin, 2004.
- S. Figedy, G. Oksa, Modern methods of signal processing in the loose part monitoring system, Prog. Nucl. Energy 46 (2005) 253-267. https://doi.org/10.1016/j.pnucene.2005.03.008
- S.-H. Shin, J.-H. Park, D.-B. Yoon, Y.-C. Choi, Mass estimation of impacting objects against a structure using artificial neural network without consideration of background noise, Nucl. Eng. Technol. 43 (2011) 343-354. https://doi.org/10.5516/NET.2011.43.4.343
- T. Tsunoda, K. Sano, O. Tsuneoka, T. Morioka, Acceleration signal characteristics for loose part impact, J. Nucl. Sci. Technol. 23 (1986) 968-978. https://doi.org/10.1080/18811248.1986.9735085
- L.R. Rabiner, A tutorial on hidden Markov models and selected application in speech recognition, Proc. IEEE 77 (1989) 257-286. https://doi.org/10.1109/5.18626
- O. Cappe, E. Moulines, T. Ryden, Inference in Hidden Markov Models, Springer, New York, 2005.
- R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, second ed., John Wiley & Sons. Inc., New York, 2001.
- J.-S. Kim, I.-K. Hwang, K.-C. Kwon, J. Lyon, Development of integrated automatic diagnosis method for loose parts monitoring system, Prog. Nucl. Energy 43 (2003) 233-242. https://doi.org/10.1016/S0149-1970(03)00035-0