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Fuzzy Wavelets for Feature Extraction and Failure Classification

In: Fuzzy Hardware

Author

Listed:
  • George Vachtsevanos
  • Vipin K. Ramani
  • Muid Mufti

Abstract

Traditionally, model-based techniques have been used for feature extraction [1]. These techniques rely solely on an accurate model of the system. Failure sensitive filters and multiple hypotheses filter detectors aim at classifying abnormal system behavior using system models. Model-based techniques perform satisfactorily as long as the model characteristics are close to the actual system. However, performance degrades rapidly if the model does not closely represent the actual system. Unfortunately, accurate models are not available for most systems. There is a growing potential for knowledge-based models instead of analytic ones. Knowledge systems have the capability of including a wider range of information sources such as input-output data, heuristics, etc.

Suggested Citation

  • George Vachtsevanos & Vipin K. Ramani & Muid Mufti, 1998. "Fuzzy Wavelets for Feature Extraction and Failure Classification," Springer Books, in: Abraham Kandel & Gideon Langholz (ed.), Fuzzy Hardware, chapter 0, pages 311-355, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-4090-8_15
    DOI: 10.1007/978-1-4615-4090-8_15
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