Journal ArticleDOI
Fault Detection Using Canonical Variate Analysis
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TLDR
Simulation results indicate that the local approach provides a very sensitive method for detecting process changes that are very sensitive in the canonical variable coefficients.Abstract:
The system identification method canonical variate analysis (CVA) has attracted much attention from researchers for its ability to identify multivariable state-space models using experimental data. A model identified using CVA can use several methods for fault detection. Two standard methods are investigated in this paper: the first is based on Kalman filter residuals for the CVA model, the second on canonical variable residuals. In addition, a third method is proposed that uses the local approach for detecting changes in the canonical variable coefficients. The detection methods are evaluated using three simulation examples; the examples consider the effects of feedback control; process nonlinearities; and multivariable, serially correlated data. The simulations consider several types of common process faults, including sensor faults, load disturbances, and process changes. The simulation results indicate that the local approach provides a very sensitive method for detecting process changes that are dif...read more
Citations
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Journal ArticleDOI
Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations
Pabara-Ebiere Odiowei,Yi Cao +1 more
TL;DR: A new monitoring technique using the Canonical Variate Analysis with UCLs derived from the estimated probability density function through kernel density estimations (KDEs) is proposed and applied to the simulated nonlinear Tennessee Eastman Process Plant.
Journal ArticleDOI
Statistical process monitoring of a multiphase flow facility
TL;DR: In this article, the capabilities of Canonical Variate Analysis (CVA) to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies.
Journal ArticleDOI
Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection
Karl Ezra S. Pilario,Yi Cao +1 more
TL;DR: An extension of CVA, called the canonical variate dissimilarity analysis (CVDA), is proposed for process incipient fault detection in nonlinear dynamic processes under varying operating conditions and has been demonstrated to outperform traditional CVA indices and other Dissimilarity-based indices in terms of sensitivity.
Journal ArticleDOI
Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification
TL;DR: In this paper, a new fault detection and identification scheme that is based on the global–local structure analysis (GLSA) model is proposed, and an energy-function-based strategy is proposed to determine the value of the introduced tuning parameter.
Journal ArticleDOI
Perspectives on process monitoring of industrial systems
TL;DR: In this paper, the authors provide some perspectives on progress in the design of process monitoring systems over the last twenty years and discuss the challenges in the field and opportunities for future research.
References
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Journal ArticleDOI
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Journal ArticleDOI
Disturbance detection and isolation by dynamic principal component analysis
TL;DR: This paper uses a well-known ‘time lag shift’ method to include dynamic behavior in the PCA model and demonstrates the effectiveness of the proposed methodology on the Tennessee Eastman process simulation.