Oscillation detection in process industries – Part I: Review of the detection methods. (June 2019)

• Record Type: Journal Article
• Authors: Dambros, Jônathan W.V.; Trierweiler, Jorge O.; Farenzena, Marcelo
• Is Part Of: Journal of process control. Volume 78(2019)
• Page Start: 108
• Page End: 123
• Publication Date: 2019-06
• Subjects: ACF autocorrelation function (or auto-covariance function) -- ARMA autoregressive–moving-average -- CWT continuous wavelet transform -- DCDM dynamic causal digraph method -- DCT discrete cosine transform -- DFT discrete Fourier transform -- DWT discrete wavelet transform -- EMD empirical mode decomposition -- EWT empirical wavelet transform -- FMEMD fast multivariate empirical mode decomposition -- GA genetic algorithm -- HMM hidden Markov model -- IAE integral absolute error -- ICA independent component analysis -- ITD intrinsic time-scale decomposition -- LMD local mean decomposition -- LZC Lempel–Ziv complexity -- MAD mean absolute deviation -- MEMD multivariate empirical mode decomposition -- MITD multivariate intrinsic time-scale decomposition -- NA-MEMD noise-assisted Multivariate EMD -- NMF non-negative matrix factorization -- PCA principal component analysis -- PSCMAP power spectral correlation map -- PSD power spectral density -- PWOD plant-wide oscillation detection -- RZC robust zero-crossing -- SFA slow feature analysis -- STSOD single time series oscillation detection -- VMD variational mode decomposition -- WPT wavelet packet transform; Oscillation detection -- Control loop performance monitoring -- Fault detection -- Process control -- Plant-wide control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.04.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Review of the works related to oscillation detection in process industry. Discussions on the definition, types, strength, and causes of oscillation. Evaluation of 10 oscillation detection methods on industrial data. Discussions on features of interest, recommended tests, and a new detection approach. Pros and cons of the methods to facilitate the choice of an appropriated one. Abstract: Oscillatory control loop is a frequent problem in process industries. Its incidence reduces product uniformity and increases both energy consumption and raw material waste. These impacts strongly reduce plant profitability, making fundamental the removal of such oscillation. The first step in oscillation removal is its detection, which should preferably be performed automatically to enable the evaluation of the entire plant. A large number of these automatic techniques have been proposed over the last years. Thus, understanding and selecting a proper technique for the desired application is very time-consuming. This work is the first of a two-part study on oscillation detection in process industries. While the second part focuses on the evaluation of the detection techniques applied to industrial data, this part mainly includes an extensive review of the works on the topic and discussions on the definition, types, strength, and causes of oscillation. Also, the techniques are clustered into groups, and their pros and cons are confronted.