| Document Date: 2011-12-15 20:30:07
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Company Intelligent Maintenance Systems / Health Management / Creative Commons / / / Event Product Issues / / Facility University of Cincinnati / / IndustryTerm energy production / k-means algorithm / algorithm processing / wind energy / sensor health monitoring applications / paired health assessment algorithm / wind profile power law / anemometer fault detection algorithms / health assessment algorithms / given site / data processing / algorithmic processing steps / merit algorithms / weighted residual processing / residual processing algorithm / residual processing / step processing methodology / health assessment algorithm / power law model / multilayer perceptron neural network / residual processing method / health monitoring algorithms / data mining / data pre-processing / power law equation / power law / autoassociative neural network / manufacturing / auto-associative neural network / k-means clustering algorithm / autoassociative neural networks / data processing steps / estimated energy production / shear health assessment algorithm / pre-processing / subsequent processing steps / / Organization Prognostics and Health Management Society / University of Cincinnati / National Science Foundation / PHM Society / / Person David Siegel / / Position original author / model the relationship / / Product state / Case / / Technology health assessment algorithm / Paired Data Set The health assessment algorithm / k-means algorithm / filtering algorithm / residual processing algorithm / developed algorithm / paired health assessment algorithm / neural network / artificial intelligence / evaluating anemometer fault detection algorithms / health monitoring algorithms / k-means clustering algorithm / data mining / merit algorithms / shear health assessment algorithm / health assessment algorithms / /
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An Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment David Siegel1 and Jay Lee1