Non-linear principal components analysis for process fault detection

Principal component analysis (PCA) has been applied widely for monitoring plant performance across a range of industrial processes. PCA is a linear technique and it is therefore not strictly applicable for handling industrial problems which exhibit significant non-linear behaviour. A novel non-linear PCA method is proposed based upon the Input-Training neural network. Multivariate statistical process control charts with non-parametric control limits are then defined to overcome the limitations of the conventional approach of defining the limits based upon the assumption of normality. A contribution plot capable of identifying the potential source of the fault in a non-linear situation is then proposed prior to applying the methodology to a continuous industrial reactor. (C) 1998 Elsevier Science Ltd. All rights reserved.