Lianfang Cai

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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 ubiquitous measurement noise and may exhibit unsatisfactory performance under the adverse effects of the measurement noise. To solve this problem,(More)
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 under the adverse effects of the measurement noise. In this paper, a process monitoring method using a new noisy(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)
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 fault detection method based on dynamic kernel slow feature analysis (DKSFA) is presented in the paper. SFA is a new feature extraction technology which can find a group of slowly varying feature outputs from the high-dimensional inputs. In order to analyze the nonlinear dynamic characteristics of the process data, DKSFA is presented which applies the(More)
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