  Principal component analysis (PCA) is largely adopted for chemical process
monitoring and numerous PCA-based systems have been developed to solve various
fault detection and diagnosis problems. Since PCA-based methods assume that the
monitored process is linear, nonlinear PCA models, such as autoencoder models
and kernel principal component analysis (KPCA), has been proposed and applied
to nonlinear process monitoring. However, KPCA-based methods need to perform
eigen-decomposition (ED) on the kernel Gram matrix whose dimensions depend on
the number of training data. Moreover, prefixed kernel parameters cannot be
most effective for different faults which may need different parameters to
maximize their respective detection performances. Autoencoder models lack the
consideration of orthogonal constraints which is crucial for PCA-based
algorithms. To address these problems, this paper proposes a novel nonlinear
method, called neural component analysis (NCA), which intends to train a
feedforward neural work with orthogonal constraints such as those used in PCA.
NCA can adaptively learn its parameters through backpropagation and the
dimensionality of the nonlinear features has no relationship with the number of
training samples. Extensive experimental results on the Tennessee Eastman (TE)
benchmark process show the superiority of NCA in terms of missed detection rate
(MDR) and false alarm rate (FAR). The source code of NCA can be found in
https://github.com/haitaozhao/Neural-Component-Analysis.git.
