Multivariate singular spectrum analysis and detrended fluctuation analysis for plant-wide oscillations denoising

Abstract

Oscillations are considered the most important indicator of poorly performing control loops. However, noise and other disturbances conceal these oscillations, thus making the detection task quite difficult. Furthermore, the efficiency of most detection and diagnosis techniques proposed in the literature is reduced considerably in the presence of noise. Therefore, denoising is recommended to make the detection task more straightforward. In this work, the multivariate singular spectrum analysis (MSSA) is employed to denoise the plant-wide oscillatory control loops. This approach stands in contrast to existing methods that typically focus on addressing noise in individual control loops. In order to improve the efficiency of MSSA, detrended fluctuation analysis (DFA) is incorporated to select only the significant components and eliminate the noise to provide a noise-free version of the multivariate data. The effectiveness of the proposed MSSA-DFA method has been verified using a numerical example and real industrial plant data.