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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))
  • 투고 : 2017.01.11
  • 심사 : 2017.05.05
  • 발행 : 2017.10.25

초록

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.

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea (NRF)

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