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Title: Multivariable MPC system performance assessment, monitoring, and diagnosis
Creator: Schaffer, J., Cinar, A.
Date: 2004-03
Publisher: ELSEVIER SCI LTD
Description: This study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a...
Show moreThis study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a benchmark and monitor model predictive control performance on-line. A performance measure based on the ratio of historical and achieved performance is used for monitoring and a ratio of design and achieved performance is used for diagnosis. Performance monitoring and diagnosis of causes for poor performance are integrated. A real-time knowledge-based system is developed to supervise monitoring and diagnosis activities. Case studies with linear and nonlinear models of an evaporator illustrate the methodology and limitations of linearity assumptions.
Endnote format citation for DOI:10.1016/j.jprocont.2003.07.003
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Title: Multivariate statistical methods for monitoring continuous processes: Assessment of discrimination power of disturbance models and diagnosis of multiple disturbances
Creator: Raich, A. C., Cinar, A.
Date: 1995-11
Publisher: ELSEVIER SCIENCE BV
Description: A new methodology was reported [1,2] for integrated use of principal components analysis (PCA) and discriminant analysis in order to determine...
Show moreA new methodology was reported [1,2] for integrated use of principal components analysis (PCA) and discriminant analysis in order to determine out-of-control status of a continuous process and to diagnose the source causes for abnormal behavior. Most of the disturbances were identified with good rates of success, with a higher success rate for step or ramp type of disturbances. Quantitative tools that evaluate overlap and similarity between high-dimensional PCA models are proposed in this communication, and their implications on determining the discrimination power of PCA models of processes operating under disturbances are discussed. Diagnosis of several disturbances occurring simultaneously is also investigated. The criterion developed provide upper limits of discrimination power of various single and multiple process disturbances. The techniques developed are illustrated by assessing the process described by the Tennessee Eastman Control Challenge problem [3].
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Title: HACCP with multivariate process monitoring and fault diagnosis techniques: application to a food pasteurization process
Creator: Tokatli, F., Cinar, A., Schlesser, J. E.
Date: 2005-06
Publisher: ELSEVIER SCI LTD
Description: Multivariate statistical process monitoring (SPM), and fault detection and diagnosis (FDD) methods are developed to monitor the critical...
Show moreMultivariate statistical process monitoring (SPM), and fault detection and diagnosis (FDD) methods are developed to monitor the critical control points (CCPs) in a continuous food pasteurization process. Multivariate SPM techniques effectively use information from all process variables to detect abnormal process behavior. Fault diagnosis techniques isolate the source cause of the deviation in process variable(s). The methods developed are illustrated by implementing them to monitor the critical control points and diagnose causes of abnormal operation of a high temperature short time (HTST) pasteurization pilot plant. The detection power of multivariate SPM and FDD techniques over univariate SPM techniques is shown and their integrated use to ensure the product safety and quality in food processes is demonstrated.
Endnote format citation for DOI:10.1016/j.foodcont.2004.04.008
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Title: Integration of multivariate SPM and FDD by parity space technique for a food pasteurization process
Creator: Kosebalaban, F., Cinar, A.
Date: 2001-03-15
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Description: Multivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect...
Show moreMultivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The methods are illustrated by monitoring the critical control points (CCP) and diagnosing causes of abnormal operation of a pilot pasteurization plant. An empirical model of the process is developed by using subspace state space system identification methods and normal process data. The process data collected under the influence of different magnitude and duration of faults in sensors and actuators are used to validate the MSPM and FD techniques. T-2 and squared prediction error (SPEN) charts are used as MSPM charts. A parity space technique for dynamic stochastic systems and dynamic trends in contribution plots of T-2 and SPEN statistics are used for FD. The detection and FD by these techniques show significant improvements over univariate methods.
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Title: Fault detection and diagnosis in a food pasteurization process with hidden Markov models
Creator: Tokatli, F., Cinar, A.
Date: 2004-12
Publisher: CANADIAN SOC CHEMICAL ENGINEERING
Description: Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is...
Show moreHidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high-temperature-short-time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.
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