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A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.
번호 | 참고문헌 | 국회도서관 소장유무 |
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1 | H. Hashemian, On-line monitoring applications in nuclear power plants, Prog. Nucl. Energy 53 (2) (2011) 167-181. | 미소장 |
2 | J.B. Coble, R.M. Meyer, P. Ramuhalli, L.J. Bond, H. Hashemian, B. Shumaker, D. Cummins, A Review of Sensor Calibration Monitoring for Calibration Interval Extension in Nuclear Power Plants. No. PNNL-21687. Pacific Northwest National Lab(PNNL), 2012. Richland, WA USA. | 미소장 |
3 | J.W. Hines, R. Seibert, Technical Review of On-Line Monitoring Techniques for Performance Assessment (NUREG/CR-6895) Vol. 1. State-Of-The-Art, US Nuclear Regulatory Committee, 2006. | 미소장 |
4 | C.K. Williams, C.E. Rasmussen, Gaussian Processes for Machine Learning, MIT Press, 2006. | 미소장 |
5 | S. Lee, J. Chai, An enhanced prediction model for the on-line monitoring method using the Gaussian process regression, Journal of Mechanical Science and Technology. 33 (2019) 2249-2257, in re-view. | 미소장 |
6 | Yi Liu, Qing-Yang Wu, Junghui Chen, Active selection of informative data for sequential quality enhancement of soft sensor models with latent variables, Ind. Eng. Chem. Res. 56 (16) (2017) 4804-4817. | 미소장 |
7 | J.W. Hines, R. Seibert, Technical Review of On-Line Monitoring Techniques for Performance Assessment (NUREG/CR-6895) Vol. 2, in: Theoretical Issues, US Nuclear Regulatory Committee, 2008. May. | 미소장 |
8 | F. Di Maio, P. Baraldi, E. Zeo, R. Seraoui, Fault detection in nuclear power plants components by a combination of statistical methods, IEEE Trans. Reliab. 62 (4)(2013) 833-845. | 미소장 |
9 | N. Sairam, S. Mandal, Thermocouple Sensor Fault Detection Using AutoAssociative Kernel Regression and Generalized Likelihood Ratio Test., Computer, Electrical & Communication Engineering (ICCECE), 2016 International Conference on, IEEE, 2016, pp. 1-6. | 미소장 |
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