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Experimental Validation of LS-SVM Based Fault Identification in Analog Circuits Using Frequency Features

  • A. S. S. Vasan
  • B. Long
  • M. Pecht
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Analog circuits have been widely used in diverse fields such as avionics, telecommunications, healthcare, and more. Detection and identification of soft faults in analog circuits subjected to component variation within standard tolerance range is critical for the development of reliable electronic systems, and thus forms the primary focus of this paper. In this paper, we have experimentally demonstrated a reliable and accurate (99 %) fault diagnostic framework consisting of a sweep signal generator, spectral estimator and a least squares-support vector machine. The proposed method is completely automated and can be extended for testing other analog circuits whose performances are mainly determined by their frequency characteristics.

Keywords

Support Vector Machine Power Spectral Density Fault Diagnosis Little Square Support Vector Machine Analog Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the more than 100 companies and organizations that support research activities at the Prognostics and Health Management Group within the Center for Advanced Life Cycle Engineering at the University of Maryland annually.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Center for Advanced Life Cycle Engineering (CALCE)University of MarylandCollege ParkUSA
  2. 2.School of Automation EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Center for Prognostics and System Health ManagementCity University of Hong KongKowloonHong Kong

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