Fuzzy Wavelets for Feature Extraction and Failure Classification

George Vachtsevanos, Vipin K. Ramani and Muid Mufti

Chapter Chapter 15 in Fuzzy Hardware, 1998, pp 311-355 from Springer

Abstract: 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.

Keywords: Feature Extraction; Wavelet Coefficient; Wavelet Function; Failure Detection; Inference Engine (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1007/978-1-4615-4090-8_15

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