Abstract
Process monitoring using nonlinear principal component analysis (NLPCA) is revisited, in particular that the score variables produced by the NLPCA model may not be statistically independent, nor follow a normal distribution. The Hotelling's T2 statistic is therefore unavailable for monitoring. This is addressed by introducing the statistical local approach into NLPCA based monitoring. The statistics from the local approach follow a normal distribution irrespective of the distribution of the score variables. This produces a Hotelling's T2 statistic with an underlying central χ2 distribution as in linear PCA case. The associated benefits are exemplified using some examples.
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