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HACCP with multivariate process monitoring and fault diagnosis techniques: application to a food pasteurization process
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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Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring
Title
Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring
Creator
Norvilas, A., Negiz, A., Decicco, J., Cinar, A.
Date
2000-08
Publisher
ELSEVIER SCI LTD
Description
An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process...
Show moreAn intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate stare space (CVSS) process models. Fault detection is based on T-2 charts of state variables. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T-2 values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This intelligent monitoring and diagnosis system can be used to monitor multivariable processes with autocorrelated, cross-correlated, and collinear data. The structure of the integrated system is described and its performance is illustrated by simulation studies.
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An intelligent system for multivariate statistical process monitoring and diagnosis
Title
An intelligent system for multivariate statistical process monitoring and diagnosis
Creator
Tatara, E., Cinar, A.
Date
2002-04
Publisher
I S A-THE INSTRUMENTATION SYSTEMS AUTOMATION SOC
Description
A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real...
Show moreA knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.
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