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Feature extraction is crucial to rotating machinery prognosis, which is an important aspect of condition monitoring as well as maintenance program, since the quality of feature will impact the result significantly. Vibration signals are commonly used as the source for feature extraction during the prognosis process, especially the energy feature of fundamental frequency (which is written as 1X), 2X,...
This paper proposed a dominant feature selection scheme to enable the high performance prognostics of machine health. Statistical features were extracted from decomposed sub-modes by wavelet transform. Fisher ratio was employed to evaluate the extracted feature vectors, and dynamic searching strategy-based genetic algorithm was used to select the optimal feature subsets on the basis of maximizing...
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