Abstract
ABSTRACTIn this work, a new fault detection method based on non‐negative matrix factorization (NMF) is presented for non‐Gaussian processes. NMF is a new dimension reduction technique that can preserve spatial relationships corresponding and retain the intrinsic structure of original data. The basic idea of our approach is to use NMF to extract the latent variables that drive a process and to combine them with process monitoring techniques. A modified alternating least squares algorithm with an order constraint and a fixed initialization is proposed for solving the NMF problem. In addition, kernel density estimation is adopted to calculate the confidence limits of defined statistical metrics for NMF‐based monitoring method. Afterwards, the proposed method is applied to the Tennessee Eastman process to evaluate the monitoring performance, comparing with principal component analysis and independent component analysis. The experiment results clearly illustrate the feasibility of the proposed method. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd.
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