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Adaptive Cone-Kernel Time-Frequency Distribution for Analyzing the Pipe-Thinning in the Secondary Systems of NPP  

Kim, Jung-Taek (한국원자력연구소)
Lee, Sang-Jeong (충남대 공대 전자공학과)
Lee, Cheol-Kwon (한국원자력연구소)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.55, no.3, 2006 , pp. 131-137 More about this Journal
Abstract
The secondary system of nuclear power plants consists of sophisticated piping systems operating in very aggressive erosion and corrosion environments, which make a piping system vulnerable to the wear and degradation due to the several chemical components and high flow rate (~10 m/sec) of the coolant. To monitor the wear and degradation on a pipe, the vibration signals are measured from the pipe with an accelerometer For analyzing the vibration signal the time-frequency analysis (TFA) is used, which is known to be effective for the analysis of time-varying or transient signals. To reduce the inteferences (cross-terms) due to the bilinear structure of the time-frequency distribution, an adaptive cone-kernel distribution (ACKD) is proposed. The cone length of ACKD to determine the characteristics of distribution is optimally selected through an adaptive algorithm using the normalized Shannon's entropy And the ACKD's are compared with the results of other analyses based on the Fourier Transform (FT) and other TFA's. The ACKD shows a better signature for the wear/degradation within a pipe and provides the additional information in relation to the time that any analysis based on the conventional FT can not provide.
Keywords
파이프 감육상태 분석;진동신호 해석;시간-주파수 해석;적응 콘-커널 분포함수;Shannon의 엔트로피;
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1 R. N. Czerwinski and D. L. Jones, 'Adaptive Cone-Kernel Time-Frequency Analysis,' IEEE Trans. on Signal Processing, Vol. 43(7), pp.1715-1719, 1995   DOI   ScienceOn
2 N. Y. Lee, et al., 'Condition Monitoring of Flow-Accelerated Corrosion of a Simulated Low Alloy Steel Piping Elbow using Electrochemical and Vibration Sensors,' SMiRT17, Prague, Czech Republic, Aug. 2003
3 D. L. Jones and T. W. Parks, 'A High Resolution Data-adaptive Time-Frequency Representation,' IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. 38(12), pp.2127-2135, 1990   DOI   ScienceOn
4 D.L. Jones and R.G. Baraniuk, 'A Simple Scheme for Adapting Time-Frequency Representations,' IEEE Trans. on Signal Processing, Vol. 42, pp.3530-3535, 1994   DOI   ScienceOn
5 T. J. Wahl and J. S. Bolton, 'The Application of the Wigner Distribution to the Identification of Structure-Borne Noise Components,' J. of Sound & Vibration, Vol. 163(1), pp.101-122, 1993   DOI   ScienceOn
6 J.T. Kim et al, 'Integrated Approach ofr on-line Condition Monitoring on Process Components and Piping,' Proc. of ANDE 2005, Jeju, Nov., 2005
7 C.K. Lee et al., 'Condition Monitoring for Check Valve Using Time-Frequency representation,' in Proc. 11th Asia-Pacific Conf. Non-Destructive Testing. Jeju, Korea, Nov. 2003
8 T. A. C. M. Claasen and W. F. G. Mecklenbrauker, 'The Wigner Distribution - A Tool for Time Frequency Signal Analysis - Part I and Part II,' Philips J. of Research, Vol. 35, pp.217-250, 276-300, 1980
9 L. Cohen, Time-Frequency Analysis, Prentice-Hall, 1995
10 S.H. Seong et al, 'Vibration Analysis for Condition Monitoring on Piping Elbow Thinning,' Proc. of ISOFIC 2005, Chungmu, Nov., 2005
11 C.K. Lee, J.T. Kim and S.J. Lee, 'Detection of a Vibration Caused by a Flow Accelerated Corrsion on the Pipes Using a Cone-Shaped Kernel Time-Frequency Distribution,' Proc. of Korean Nuclear Society, Jeju, May, 2005
12 Shie Qian, Time-Frequency and Wavelet Transform, Prentice-Hall PTR, 2002
13 H. I. Choi and W. J. Williams, 'Improved Time Frequency Representation of Multicomponent signals Using Exponential Kernels,' IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. 37(6), pp.862-871. 1989   DOI   ScienceOn
14 Y. Zhao, L. E. Atlas, and R. J. Marks II, 'The Use of Cone-shaped kernels for Generalized Time-Frequency Representations of Nonstationary and Signals,' IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. 38(7), pp.1084-1091, 1990   DOI   ScienceOn