Abstract
This chapter presents the state-of-art progress in multivariate data-based process monitoring. More specially, the progress in fault detection and diagnosis by the multivariate statistical process monitoring and machine learning-based tools is discussed. First, the commonly used data-driven fault detection and diagnosis tools have been reviewed. Then a generalized framework for statistical process monitoring tools is demonstrated, followed by a comparative performance of different tools. Finally, algorithms to build the supervised and unsupervised neural networks are presented with their applications to process systems.
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