Removal of transient disturbances from oscillating measurements using nearest neighbors imputation

Cecı́lio, Inês; Ottewill, James R.; Fretheim, Harald; Thornhill, Nina F.

doi:10.1016/j.jprocont.2016.04.013

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…The prediction of a time series from its own past is known as self-predictability (Kantz and Schreiber, 2003). Applications of this property include removing transient disturbances from oscillatory time series (Cecílio et al, 2015), and testing the non-linearity of a time series (Thornhill, 2005).…”

Section: Self and Mutual Predictabilitymentioning

confidence: 99%

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

Cecı́lio

Ottewill

Thornhill

2016

Control Engineering Practice

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This paper proposes a multi-rate method to identify the propagation path of a persistent disturbance in an enlarged system envelope which includes the process plant and its electromechanical equipment. The need to integrate process and equipment diagnosis has been highlighted by industrial commentators.However, process and electromechanical measurements often have different sampling rates. The multi-rate method proposed extends a state-of-the-art propagation path method so that it combines fast-sampled electromechanical measurements and slow-sampled process measurements. The method is based on non-linear mutual prediction, which yields the directionality in the relationship between two time series. The method was demonstrated and validated, giving the expected outcome in an experimental case study, in which the root cause and propagation path of the disturbance were known.

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Section: Self and Mutual Predictabilitymentioning

confidence: 99%

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

Cecı́lio

Ottewill

Thornhill

2016

Control Engineering Practice

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“…The prediction of a time series from its own past is known as self-predictability (Kantz and Schreiber, 2003). Applications of this property include removing transient disturbances from oscillatory time series (Cecílio et al, 2014b), and testing the nonlinearity of a time series (Thornhill, 2005).…”

Section: Self and Mutual Predictabilitymentioning

confidence: 99%

Determining the propagation path of a disturbance in multi-rate systems ★ ★The authors gratefully acknowledge the financial support from the Portuguese Foundation for Science and Technology (FCT) under Fellowship SFRH/BD/61384/2009 and the Marie Curie FP7-IAPP project “REAL-SMART - Using real-time measurements for monitoring and management of power transmission dynamics for the Smart Grid”, Contract No: PIAP-GA-2009-251304.

2015

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Several methods to derive the propagation path of a disturbance in process plants are applicable only to systems whose measurements are all available with the same sampling rate. This paper proposes a multi-rate method to identify the propagation path when measurements have different sampling rates. This is relevant for including in the analysis fast-sampled measurements from electromechanical equipment. The method is based on non-linear mutual prediction, which yields the directionality in the relationship between two time series. The method was demonstrated and validated, giving the expected outcome in an experimental case study, in which the root cause and propagation path of the disturbance were known.

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“…In power system protection and power quality assessment, the objective is the detection of transients, whereas the oscillatory components reflect the nominal behavior of power, voltage, or current signals in an AC power system. Cecílio et al propose a method based on nearest neighbors to detect transients, and a method based on nearest neighbor imputation is used to remove transients …”

Section: Introductionmentioning

confidence: 99%

“…Ceci ́lio et al propose a method based on nearest neighbors to detect transients, 20 and a method based on nearest neighbor imputation is used to remove transients. 25 Common industrial practice is either to exclude from the analysis time intervals exhibiting transients or to completely drop out signals with transients. 26 However, to allow accurate oscillation detection, the oscillation should persist during eight cycles at minimum and excluding time intervals with transients can hinder that.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

Zhou

Chioua²,

Bauer

et al. 2019

Ind. Eng. Chem. Res.

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Oscillations occurring in industrial process plants often reflect the presence of severe disturbances affecting process operations. Accurate detection and root-cause analysis of oscillations is of great interest for the economic viability of the process operation. Standard oscillation detection and root cause analysis methods require a large enough number of data samples. Unrelated transient changes superimposed on the oscillation pattern reduce the number of useful data samples. The present paper proposes simple heuristic methods to effectively detect and remove two types of transient changes from oscillatory signals, namely step changes and spikes. The proposed methods are used to pre-process oscillatory time series. The accuracy gained when using auto-correlation function method for oscillation detection 1 and transfer entropy method for oscillation propagation 2 is experimentally evaluated. The methods are carried out on a 1.3-Butadiene production process where several measurements showed an established oscillation occurring after a production level change.

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Removal of transient disturbances from oscillating measurements using nearest neighbors imputation

Cited by 4 publications

References 23 publications

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process

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