Control
valves suffer from wear and aging as the valves open and
close. Such continuous movements result in many operational issues,
and one of the widely known problems is stiction nonlinearity. Stiction
results in inferior quality of the products, large rejection rates,
increased energy consumption, and reduced profitability. In this paper,
a simple algorithm that combines preprocessing and postprocessing
as well as average crossing autocovariance (AC) together with the
nonlinear principal component analysis (NLPCA) is investigated. The
results obtained from the simulated and industrial case studies show
that the proposed NLPCA-AC method has favorable proficiencies for
control valve stiction detection.
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