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As an effective nonlinear dynamic data analysis tool, kernel slow feature analysis (KSFA) has achieved great success in continuous process monitoring field during recent years. However, its application to batch process monitoring is unexploited, which is a more challenging task because of the complicated characteristics of batch process data. In this paper, we propose a novel batch process monitoring...
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 augmented matrix to consider the dynamic characteristic...
Invariant features of temporally varying signals are very useful for process monitoring. A novel nonlinear process fault diagnosis method is proposed in this paper based on slow feature analysis (SFA) which is a new invariant learning method. In the proposed method, input-output transformation functions are optimized to extract the nonlinear slowly varying components as invariant features. Based on...
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