This study addresses the underexplored challenge of inherent dynamics in industrial processes through an innovative attention-based latentvariablemodeling method. Utilizing attention mechanisms, the method articulat...
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ISBN:
(纸本)9798331540845;9789887581598
This study addresses the underexplored challenge of inherent dynamics in industrial processes through an innovative attention-based latentvariablemodeling method. Utilizing attention mechanisms, the method articulates time-variant dynamical relationships among samples. The framework extends attention-based dynamical inner principal component analysis to extract latentdynamical features, integrating them with static features obtained through static principal component analysis. This results in comprehensive monitoring statistics for online applications. Numerical simulations and real-world application in an industrial ethylene oxychlorination process demonstrate the proposed method's efficacy. Comparative analysis highlights its advantages and superior performance over existing methods. This innovative approach provides more accurate insights into complex industrial processes, promising advancements in data-driven modeling within the field.
Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anom...
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Plant-wide process data are usually high dimensional with dynamics residing in a reduced dimensional latent space. In this paper, we propose a novel procedure for diagnosing and troubleshooting plant-wide process anomalies using dynamic embedded latent feature analysis (DELFA). To remove the impact of ex-ternal disturbances or exogenous variables, a dynamic inner canonical correlation analysis algorithm with exogenous variables is proposed. Composite loadings and composite weights are derived and applied for diagnosing a feature that is contained in several latentvariables. The dynamic embedded latent features are usually related to poor control performance or malfunctioning control instrumentation. The proposed DELFA procedure with dynamiclatent scores and composite loadings is applied to two industrial datasets of a chemical plant before and after a troubled control valve was fixed. The case study demonstrates convincingly that latentdynamic features are powerful for troubleshooting of process anomalies and di-agnosing their causes in a plant-wide setting. (c) 2021 Elsevier Ltd. All rights reserved.
After introducing process data analytics using latentvariable methods and machine learning, this paper briefly review the essence and objectives of latentvariable methods to distill desirable components from a set o...
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After introducing process data analytics using latentvariable methods and machine learning, this paper briefly review the essence and objectives of latentvariable methods to distill desirable components from a set of measured variables. These latentvariable methods are then extended to modeling high dimensional time series data to extract the most dynamiclatent time series, of which the current values are best predicted from the past values of the extracted latentvariables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods. The extracted features reveal hidden information in the data that is valuable for understanding process variability. (C) 2017 Elsevier Ltd. All rights reserved.
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