Fault Monitoring Techniques for Nuclear Components

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 14, 2013
Gee-Yong Park Jung Taek Kim

Abstract

In this paper, we describe our previous studies for the development of an analysis algorithm and the application of a fault monitoring technique. Various signal processing methods have been implemented in the so-called monitoring tools to monitor and analyze abnormal conditions of components in nuclear power plants (NPPs). One of the analysis methods were devised by us for the efficient analysis of transient signals from NPP process components. This method, the adaptive cone-kernel distribution, is presented in this paper along with the description of the monitoring tool. Then, some application results using the monitoring tool are presented. As another application, the fault monitoring technique applied to the agitator driving system of a thermal chemical reduction reactor is also presented though this technique is not integrated in the monitoring tool yet.

How to Cite

Park, G.-Y. ., & Taek Kim, J. . (2013). Fault Monitoring Techniques for Nuclear Components. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2332
Abstract 83 | PDF Downloads 76

##plugins.themes.bootstrap3.article.details##

Keywords

feature extraction, Adaptive CKD, Fault Monitoring, Agitator Driving System

References
Burrus, C. S., Gopinath, R. A., & Guo, H. (1998), Introduction to Wavelets and Wavelet Transforms, Prentice Hall.

Claasen, T. A. C. M., & Meckleubrauker, W. F. G. (1980), The Wigner Distribution – A Tool for Time-Frequency Signal Analysis; Part III-Relations with Other Time- Frequency Signal Transforms. Philips J. of Research, vol.35, no.6, pp.372~389.

Cohen, L. (1995), Time-Frequency Analysis, Prentice-Hall. Choi, H. I., & Williams, W. J. (1989), Improved Time- Frequency Representation of Multicomponent Signals using Exponential Kernels. IEEE Transactions on Acoustics, Speech, and Signal Processing, vo.37, no.6,pp.862~871.

Czerwinski, R. N., & Jones, D. L. (1995), Adaptive Cone Kernel Time-Frequency Analysis. IEEE Transactions on Signal Processing, vol.43, no.7, pp.1715~1719

Hlawatsch, F., & Boudreaux-Bartels, G. F. (1992), Linear and Quadratic Time-Frequency Signal Representations. IEEE SP Magazine, pp.21~67, April.

Loughlin, P. J., Pitton, J. W., & Atlas, L. E. (1993), Bilinear Time-Frequency Representations: New Insights and Properties. IEEE Transactions on Signal Processing, vol.41, no.2, pp.750~767.

Moon, B. S. (1998), A Curve Smoothing Method by Using Fuzzy Sets. Fuzzy Sets and Systems, vol.96, pp.353~358.

Park, G. Y., et al. (2003), Fault Diagnosis for Agitator Driving System in a High Temperature Thermal Reactor. Nuclear Engineering and Technology, vol.35, no.5, pp.454~470.

Zhao, Y., Atlas, L. E., & Marks II, R. J. (1990), The Use of Cone-Shaped Kernels for Generalized Time-Frequency Representations of Nonstationary Signals. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.38, no.7, pp.1084~1091.
Section
Technical Research Papers