Abstract
In this letter, we present a novel approach of valve stiction detection using wavelet technology. A new non-invasive method is developed with the closed-loop normal operating data. The redundant dyadic discrete wavelet transform is used to decompose the data at different resolution scales. Based on the Lipschitz regularity theory, wavelet coefficients analysis across scales is performed to detect the jumps in the controlled variables. Adaptive wavelet de-noising is then applied to the data. Features of the valve stiction patterns are extracted from the de-noised data and the valve stiction probability is calculated.
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