Detection of multiple oscillations in control loops

Thornhill, Nina F.; Huang, Biao; Zhang, H

doi:10.1016/s0959-1524(02)00007-0

Cited by 212 publications

(173 citation statements)

References 15 publications

Supporting

Mentioning

170

Contrasting

Unclassified

Order By: Relevance

“…It can also remove non-stationary low-frequency trends such as those caused by daily temperature variations. For the autocovariance functions, the interference must be removed from both positive and negative frequencies in the two-sided power spectrum before the inverse FFT (Thornhill, Huang, & Zhang, 2002).…”

Section: Autocovariance Pca-formulationmentioning

confidence: 99%

Spectral principal component analysis of dynamic process data

Thornhill

Shah

Huang

et al. 2002

Control Engineering Practice

130

View full text Add to dashboard Cite

This article describes principal component analysis (PCA) of the power spectra of data from chemical processes. Spectral PCA can be applied to the measurements from a whole unit or plant because spectra are invariant to the phase lags caused by time delays and process dynamics. The same comment applies to PCA using autocovariance functions, which was also studied. Two case studies are presented. One was derived from simulation of a pulp process. The second was from a refinery involving 37 tags. In both cases, PCA clusters were observed which were characterised by distinct spectral features. Spectral PCA was compared with PCA using autocovariance functions. The performance was similar, and both offered an improvement over PCA using the time domain signals even when time shifting was used to align the phases. r

show abstract

Section: Autocovariance Pca-formulationmentioning

confidence: 99%

Spectral principal component analysis of dynamic process data

Thornhill

Shah

Huang

et al. 2002

Control Engineering Practice

130

View full text Add to dashboard Cite

show abstract

“…The method of Miao and Seborg, 9 which only considers first one and a half oscillation periods, achieved the correct diagnosis. In contrast to that, the method presented in ref 10, which requires at least 10 oscillation periods in the autocorrelation function to present, was not able to recognize oscillations. The algorithm of Salsbury and Singhal 11 obtained a model, describing fairly well the first period of the autocorrelation function.…”

Section: Results Of the Comparison Of The Proposedmentioning

confidence: 80%

“…In more detail, the approach explores the lengths and the areas of the subsequent upper and lower half-periods. Miao and Seborg 9 consider the decay ratio of the autocorrelation function of the signal, and Thornhill et al 10 method is based on the regularity of zerocrossings in the autocorrelation function. Salsbury and Singhal 11 compute the damping factor of the second-order ARMA model identified from the autocorrelation function.…”

Section: Introductionmentioning

confidence: 99%

Robust Oscillation Detection Index and Characterization of Oscillating Signals for Valve Stiction Detection

Zakharov

Jämsä‐Jounela

2014

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Detection of oscillation is a necessary step for determining valve stiction, which is a common problem in process industries. More specifically, many stiction detection methods assume persistency of the oscillation pattern in the control loop signals, which is a necessary condition to achieve reliable detection results. However, the existing oscillation detection methods do not study the persistency of the oscillation patterns directly. Instead, these methods try to discover the presence of specific periodic characteristics in the signals, their autocorrelation, or power-spectrum. This paper aims to propose an oscillation detection method that directly evaluates the similarity of the shapes of subsequent oscillation periods by means of correlation coefficient. The proposed oscillation detection method is compared against five other methods reported in the literature. Furthermore, the stiction detection methods assuming oscillation in control loops have different robustness to the disturbances corrupting the analyzed signals. In order to prepare a basis for selecting the right stiction detection methods for the available data automatically, the paper introduces two indexes quantifying mean-nonstationarity and presence of noise in oscillating signals.

show abstract

“…Oscillation analysis: The purpose of oscillation analysis [6,7] is to characterize the oscillations present in the data set. It determines the intervals between zero crossings of the autocovariance function and the regularity of the zero crossings.…”

Section: Oscillation Analysismentioning

confidence: 99%

Cause and effect analysis of chemical processes - analysis of a plant-wide disturbance

Bauer

Thornhill

Meaburn

2005

IEE Seminar on Control Loop Assessment and Diagnosis

View full text Add to dashboard Cite

In continuous chemical processes, disturbances in the process conditions can propagate widely and cause secondary upsets in remote locations. The aim of this paper is to apply some recent data-driven methods for detection and diagnosis of process disturbances using historical process data that have been proving successful in a range of applications. An industrial case study is presented in which a plant-wide control system disturbance caused by the presence of a recycle was successfully located and then verified by further plant testing.

show abstract

Detection of multiple oscillations in control loops

Cited by 212 publications

References 15 publications

Spectral principal component analysis of dynamic process data

Spectral principal component analysis of dynamic process data

Robust Oscillation Detection Index and Characterization of Oscillating Signals for Valve Stiction Detection

Cause and effect analysis of chemical processes - analysis of a plant-wide disturbance

Contact Info

Product

Resources

About