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This paper applies a mean-covariance decomposition (MCD) modeling method using data within moving windows to analyze the capacity fade process. The proposed approach directly examines the variances and correlations in data of interest and reparameterize the correlation matrix in hyper-spherical coordinates using angle and trigonometric functions. To improve the interpretation of the prognostics model, the mean function is obtained based on physics of failure. Non-parametric methods are used to characterize the log variance and correlation through the number of cycles and time lags between capacity measurements, respectively. 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