Diagnosis of plant-wide oscillations by combining multivariate empirical mode decomposition and delay vector variance

Aftab, Muhammad Faisal; Hovd, Morten; Sivalingam, Selvanathan

doi:10.1016/j.jprocont.2019.01.001

Cited by 16 publications

(9 citation statements)

References 22 publications

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“…Furthermore, the plant data from ind_rep2 was used to isolate the source of nonlinearity, and the results were found consistent with those of ref . This method was further enhanced to deal with nonstationary data and multiple oscillations by integrating it with NA-MEMD …”

Section: Diagnosis Techniquessupporting

confidence: 72%

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Bounoua

Aftab

Omlin

2022

Ind. Eng. Chem. Res.

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Optimal operations of industrial control systems require rigorous monitoring to ensure safety, increase profitability, and minimize plant maintenance downtime. Thus, controller performance monitoring has been actively pursued by the research community, resulting in increased research publications over the past two decades. The availability of large data sets, the so-called big data era, has led impetus to the data-driven domain of controller performance monitoring. In this paper, a comprehensive review of the data-driven research in the CPM domain is presented. To illustrate the rationale behind the research efforts, a succinct explanation of the faults observed in control loops and their influence on overall controller performance is also presented. The paper then provides the publicly available data repositories adopted by most researchers to assess and compare their performance monitoring techniques realistically. Moreover, a review of the most eminent techniques proposed so far concerning both detection and diagnosis phases is presented.

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Section: Diagnosis Techniquessupporting

confidence: 72%

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Bounoua

Aftab

Omlin

2022

Ind. Eng. Chem. Res.

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“…The closer it is to 1, the more significant the oscillation degree is. According to references [5], [21], if ζ k,q > ζ , where ζ = 0.58, the corresponding mode can be regraded to be oscillatory. When the oscillations are detected, they can be diagnosed whether the oscillation is caused by nonlinearity problem.…”

Section: B Detecting and Diagnosing Unit-wide Oscillationsmentioning

confidence: 99%

“…Inspired by the idea that oscillations caused by nonlinearities contain higher order harmonics [35], this paper presents an SSA-MVMD-based strategy to diagnose the unit-wide oscillations by investigating the oscillation period relationship among different modes. More specifically, if ∆t k,q represents the time interval between two successive zero crossings of the significant oscillating mode, then the average time P for I = 11 such intervals will be given by [5] Pk,q = 2…”

Section: B Detecting and Diagnosing Unit-wide Oscillationsmentioning

confidence: 99%

“…Unit-wide oscillation detection and diagnosis technology has been developed for more than twenty years [5]. A direct idea for unit-wide oscillation detection is to detect each control loop one by one through using the single-loop oscillation analysis methods, such as integral absolute error (IAE) [6], zero-crossing [7], auto-covariance function (ACF) [8], autoregressive moving average model [9], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

Lin

Sun²,

2022

IEEE Access

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In process control system, nonlinearity-induced unit-wide oscillations are a common fault, which degrades the control performance and threaten the stability. It is important to detect and diagnose the nonlinearity-induced unit-wide oscillations to improve the process control performance. In this paper, a novel method, termed as SSA-MVMD, is proposed by combining the sparrow search algorithm (SSA) and multivariate variational mode decomposition (MVMD) to detect and diagnose the nonlinearity-induced unit-wide oscillations. MVMD is an advanced signal decomposition and time-frequency method. However, its performance is affected by the mode number K and penalty coefficient α. SSA is adopted to optimize the parameters of MVMD. Then, a novel SSA-MVMD-based detector is presented to detect and diagnose the nonlinearity-induced unit-wide oscillations. The proposed method is model-free and data-driven thus requiring no prior knowledge about the process dynamics. Compared with the latest related works, the proposed method can better decompose the multivariate nonstationary signals and adaptively analyze the unit-wide oscillations. In the end, the effectiveness and advantages are demonstrated by simulations as well as industrial cases.

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“…Aiming at these problems, the empirical mode decomposition (EMD) method with adaptive decomposition has been proposed, which does not require the predetermination of the basis function . The EMD is an efficient method for data preprocessing and analysis in many fields, such as fault diagnosis and biological signal processing . Meanwhile, EMD can cope with both nonlinear and nonstationary signals. , However, traditional EMD can only deal with univariate signals.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of the Cement Calcination Process for Reducing NO_x Emission Using an Improved Just-In-Time Gaussian Mixture Regression

Zheng

Lang

et al. 2020

Ind. Eng. Chem. Res.

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The cement calcination process suffers serious pollutant emission problems, especially nitrogen oxide (NO x ) emission. Traditional methods mainly used physical modeling to optimize the operation of a cement calcination process for NO x reduction. However, physical modeling of NO x emission in the rotary kiln is too complicated because of the difficulties with determining model parameters. To address this challenge, the present study proposes the use of data-driven modeling and model-based real-time cement calcination process optimization (RTO) for reducing NO x emission. The Gaussian mixture regression model based on an improved just-in-time learning is introduced for modeling NO x in the kiln tail. Data preprocessing based on multivariate empirical mode decomposition is used, and an improved similarity strategy taking into account time-space information is utilized for local learning relevant sample selection to address problems associated with nonlinear and time-varying characteristics. The RTO problem is solved by a particle swarm optimization algorithm, and optimized set values of decision variables are obtained. Finally, the proposed modeling and optimization approach are applied to practical cement calcination process data. The result shows that the proposed modeling strategy has better performance than the traditional strategy, and the optimization approach performs better than previous conditions in calcination NO x emission reduction.

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Diagnosis of plant-wide oscillations by combining multivariate empirical mode decomposition and delay vector variance

Cited by 16 publications

References 22 publications

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

Modeling and Optimization of the Cement Calcination Process for Reducing NO_x Emission Using an Improved Just-In-Time Gaussian Mixture Regression

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Diagnosis of plant-wide oscillations by combining multivariate empirical mode decomposition and delay vector variance

Cited by 16 publications

References 22 publications

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Controller Performance Monitoring: A Survey of Problems and a Review of Approaches from a Data-Driven Perspective with a Focus on Oscillations Detection and Diagnosis

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

Modeling and Optimization of the Cement Calcination Process for Reducing NOx Emission Using an Improved Just-In-Time Gaussian Mixture Regression

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Product

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Modeling and Optimization of the Cement Calcination Process for Reducing NO_x Emission Using an Improved Just-In-Time Gaussian Mixture Regression