A method for measuring the robustness of diagnostic models for predicting the break size during LOCA

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Published Oct 2, 2017
Xiange Tian Victor Becerra Nils Bausch Gopika Vinod T.V. Santhosh

Abstract

The diagnosis of loss of coolant accidents (LOCA) in nuclear reactors has attracted a great deal of attention in condition monitoring of nuclear power plants (NPPs) because the health of cooling system is crucial to the stability of the nuclear reactor. Multi-layer perceptron (MLP) neural networks have commonly been applied to LOCA diagnosis. The data used for training these models consists of a number of time-series data sets, each for a different break size, with the transient behavior of different measurable variables in the coolant system of the reactor following a LOCA. It is important to select a suitable architecture for the neural network that delivers robust results, in that the predicted break size is deemed to be accurate even for a break size that is not included in the training data sets. The objective of this paper is to present a simple method for measuring the robustness of diagnostic models for predicting the break size during the loss of coolant accidents. A robustness metric is proposed based on the leave-one-out approach and the mean squared error resulting from a diagnostics model. Using this metric it becomes possible to compare the robustness of different diagnostic models. Given data obtained from a high fidelity simulation of the coolant system of a nuclear reactor, four different diagnostic models are obtained and their properties compared and discussed. These models include a fully connected multi-layer perceptron with one hidden layer, a fully connected multi-layer perceptron with two hidden layers, a multi-layer perceptron with one hidden layer that is pruned using the optimal brain surgeon algorithm, a group method of data handling (GMDH) neural network, and an adaptive network based fuzzy inference system (ANFIS).

How to Cite

Tian, X., Becerra, V., Bausch, N., Vinod, G., & Santhosh, T. (2017). A method for measuring the robustness of diagnostic models for predicting the break size during LOCA. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2186
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Keywords

Loss of coolant accident, Nuclear power plant, Multilayer Perception, Group method of data handling, Optimal brain surgeon

References
Moshkbar-Bakhshayesh, K., & Ghofrani, M. B. (2013). Transient identification in nuclear power plants: A review. Progress in Nuclear Energy, 67, 23–32. https://doi.org/10.1016/j.pnucene.2013.03.017
Lee, S. H., No, Y. G., Na, M. G., Ahn, K. I., & Park, S. Y. (2011). Diagnostics of Loss of Coolant Accidents Using SVC and GMDH Models. IEEE Transactions on Nuclear Science, 58(1), 267–276. https://doi.org/10.1109/TNS.2010.2091972
Lu, B., & Upadhyaya, B. R. (2005). Monitoring and fault diagnosis of the steam generator system of a nuclear power plant using data-driven modeling and residual space analysis. Annals of Nuclear Energy, 32(9), 897–912. https://doi.org/10.1016/j.anucene.2005.02.003
Lee, S. H., No, Y. G., Na, M. G., Ahn, K. I., & Park, S. Y. (2011). Diagnostics of Loss of Coolant Accidents Using SVC and GMDH Models. IEEE Transactions on Nuclear Science, 58(1), 267–276. https://doi.org/10.1109/TNS.2010.2091972
da Costa, R. G., Mol, A. C. de A., de Carvalho, P. V. R., & Lapa, C. M. F. (2011). An efficient Neuro-Fuzzy approach to nuclear power plant transient identification. Annals of Nuclear Energy, 38(6), 1418–1426. https://doi.org/10.1016/j.anucene.2011.01.027
Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993. https://doi.org/10.1109/72.329697
Hassibi, B., Stork, D. G., & Wolff, G. J. (1993). Optimal Brain Surgeon and general network pruning. In IEEE International Conference on Neural Networks (pp. 293–299 vol.1). https://doi.org/10.1109/ICNN.1993.298572
Norgaard, M., Ravn, O., Poulsen, N. K., and Hansen, L. K., (2000). Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook, (Springer-Verlag, London), pp. 102-111.
Jang, J. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. http://dx.doi.org/10.1109/21.256541
MathWorks, (2016). Fuzzy Logic Toolbox™: User's Guide (R2016a). Retrieved March 10, 2017 from http://cn.mathworks.com/help/pdf_doc/fuzzy/fuzzy.pdf
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116–132. https://doi.org/10.1109/TSMC.1985.6313399
Celikyilmaz A., & Türksen I. B. (2009) Modelling uncertainity with fuzzy logic: with recent theory and applications. Germany: Springer.
Santosh, T. V., Srivastava, A., Sanyasi Rao, V. V. S., Ghosh, A. K., & Kushwaha, H. S. (2009). Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks. Reliability Engineering & System Safety, 94(3), 759–762. https://doi.org/10.1016/j.ress.2008.08.005
Santhosh, T. V., Kumar, M., Thangamani, I., Srivastava, A., Dutta, A., & Verma, V. et al. (2011). A diagnostic system for identifying accident conditions in a nuclear reactor. Nuclear Engineering and Design, 241(1), 177-184. http://dx.doi.org/10.1016/j.nucengdes.2010.10.024
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Technical Research Papers