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Independent component analysis Kurtosis Fourth order cumulant Fault detection time Fault detection rate a b s t r a c t Independent component analysis (ICA) is an effective feature extraction tool for process monitoring. However, the conventional ICA-based process monitoring methods usually adopt noise-free ICA models and thus may perform unsatisfactorily(More)
Keywords: Fault detection Independent component analysis Mixing matrix Measurement noise Time-delayed covariance matrices Kurtosis a b s t r a c t Fast independent component analysis (FastICA) is an efficient feature extraction tool widely used for process fault detection. However, the conventional FastICA-based fault detection method does not consider the(More)
For conventional post-nonlinear independent component analysis (ICA) methods, the mutual information (MI) of separated signals is estimated by using higher order statistics (HOS). These methods are sensitive to the initial parameters of separating matrix. An improved method based on Gaussian Mixture Model (GMM) is proposed in this paper to solve this(More)
A kernel independent component analysis (KICA) is widely regarded as an effective approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based monitoring methods treat every KIC equally and cannot highlight the useful KICs associated with fault information. Consequently, fault information may not be explored effectively, which may(More)
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