Nonlinear system monitoring using multiscaled principal components analysis based on neural network

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In this paper, we propose a new method based on multiscaled principal component analysis for nonlinear systems analysis. We introduce nonlinear PCA based on neural networks and discrete wavelet transform. The data matrix describing a nonlinear process is decomposed into five wavelet resolution levels. The neural PCA is applied to each coefficient of details and approximations; we select only the scales having a defect to reconstruct the data matrix. Neural PCA is again applied to the new matrix to determine the defective variables, which are detected using the square predictive error (SPE) statistic and identified using the contributions calculation method. This method is applied to a biological process and shows efficient results.