Non-linear principal components analysis for process fault detection

doi:10.1016/S0098-1354(98)00164-1

Non-linear principal components analysis for process fault detection

1998-03-01

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DOI：10.1016/S0098-1354(98)00164-1

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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.

Keyword:

non-linear principal components analysis

fault detection

multivariate statistical process control

Non-linear principal components analysis for process fault detection

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