Abstract:
The cost effective benefits of process monitoring will never be over emphasised.
Amongst monitoring techniques, the Independent Component Analysis (ICA) is an
efficient tool to reveal hidden factors from process measurements, which follow
non-Gaussian distributions. Conventionally, most ICA algorithms adopt the
Principal Component Analysis (PCA) as a pre-processing tool for dimension
reduction and de-correlation before extracting the independent components (ICs).
However, due to the static nature of the PCA, such algorithms are not suitable
for dynamic process monitoring. The dynamic extension of the ICA (DICA), similar
to the dynamic PCA, is able to deal with dynamic processes, however
unsatisfactorily. On the other hand, the Canonical Variate Analysis(CVA) is an
ideal tool for dynamic process monitoring, however is not sufficient for
nonlinear systems where most measurements follow non-Gaussian distributions. To
improve the performance of nonlinear dynamic process monitoring, a state space
based ICA (SSICA) approach is proposed in this work. Unlike the conventional
ICA, the proposed algorithm employs the CVA as a dimension reduction tool to
construct a state space, from where statistically independent components are
extracted for process monitoring. The proposed SSICA is applied to the Tennessee
Eastman Process Plant as a case study. It shows that the new SSICA provides
better monitoring performance and detect some faults earlier than other
approaches, such as the DICA and the CVA. (C) 2010 Elsevier B.V. All rights
reserved.