Markov chain-based mass estimation method for loose part monitoring system and its performance

Nuclear Engineering and Technology

Soon-Woo Han¹  Doo-Byung Yoon¹  To Kang¹  Jin-Ho Park¹  Sung-Hwan Shin² 

[1] Advanced Condition Monitoring and Diagnostics Lab., Korea Atomic Energy Research Institute (KAERI), Daedeok-daero 989, Yuseong-gu, Daejeon 34057, Republic of Korea;Department of Automotive Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea;

关键词: Discrete Cosine Transform;    Loose Part Monitoring System;    Markov Decision Process;    Mass Estimation;    Reactor Vessel;   

DOI  :  10.1016/j.net.2017.05.005

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown