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Principal component analysis (PCA) is a technique commonly used for fault detection and classification (FDC) in highly automated manufacturing. Because PCA model building and adaptation rely on eigenvalue decomposition of parameter covariance matrices, the computational effort scales cubically with the number of input variables. As PCA-based FDC applications monitor systems with more variables, or...
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