Classification of Steam Generator Tube Defects for Real-Time Applications Using Eddy Current Test Data and Self-Organizing Maps

  • Authors:

  • Roberto N. De Mesquita;Daniel K. S. Ting;Eduardo L. L. Cabral;Belle R. Upadhyaya

  • Affiliations:

  • Nuclear Engineering Center, Nuclear and Energetic Research Institute (IPEN) and Mechanical Engineering Department, University of São Paulo, São Paulo, Brazil rnavarro@ipen.brdksting@ipen.br;Mechanical Engineering Department, University of São Paulo, São Paulo, Brazil elcabral@usp.br;Nuclear Engineering Department, The University of Tennessee, Knoxville, USA bupadhya@utk.edu

  • Venue:
  • Real-Time Systems
  • Year:
  • 2004

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Abstract

A new classification method, for isolating steam generator tube defects in nuclear power plants using Eddy Current Test (ECT) signals, has been developed. The method uses Self-Organizing maps (SOM) with different data signatures to identify and classify these defects. A multiple inference system is proposed which evaluates different extracted characteristic SOMs to infer the defect type. Wavelet zero-crossing representation, a linear predictive coding (LPC), and other basic signal representations, such as magnitude and phase, are used to construct characteristic vectors that combine one or more of these features. These vectors are evaluated for their ability to classify tube defects and the ones with the best performance are used in the multiple inference system. The effectiveness of the method is demonstrated by applications of the characteristic maps to ECT data from various cases of tube defects in pressurized water reactor plant steam generators. The developed algorithm enables real-time applications such as fast tube defects classification systems and visualization of ECT signal feature prototypes, which may improve the speed of time-critical decision making during power plant maintenance outages.