Abstract
A nonlinear multiscale principal component analysis (NLMSPCA) methodology is proposed for process monitoring and fault detection based on multilevel wavelet decomposition and nonlinear principal component analysis via an input-training neural network. Performance monitoring charts with non-parametric control limits are applied to identify the occurrence of non-conforming operation prior to interrogating differential contribution plots to help identify the potential source of the fault. A novel summary display is used to present the information contained in bivariate graphs in order to facilitate global visualization. Positive results were achieved through assessing the capabilities of the monitoring scheme on a nonlinear industrial process.
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