An ARMA Model Integrated MPCA Approach to Fault Prediction in Batch Processes

Hui Guo; Hong Guang  Li

doi:10.4028/www.scientific.net/AMR.765-767.2341

Paper Titles

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Integrating Thermal Convection Non-Floating Type Inclinometer and RFID Tag
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Power Cable Fault Locator Based on Synchronization Principle of Sound and Magnetic
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An ARMA Model Integrated MPCA Approach to Fault Prediction in Batch Processes
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Simulation and Design of Wireless Energy Transmission for Implantable Micro-Electromechanical Devices
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Technology Research on Intelligent Infrared Gas Analyzer
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An Improved Neural Network Algorithm and its Application in Fault Diagnosis
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The Research of Blood Glucose Monitoring System Based on Biosensor
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767An ARMA Model Integrated MPCA Approach to Fault...

An ARMA Model Integrated MPCA Approach to Fault Prediction in Batch Processes

Abstract:

To on-line forecast faults in batch processes, we introduced integrated models combining MPCA and ARMA. Time series of T² and SPE statistics obtained from MPCA models based on variable cross-correlations are employed to build univariate ARMA models in terms of their auto-correlations, predicting the h-step-ahead values implying process dynamic behaviors. The integrated models not only take advantage of both powerful data compression and prominent prediction ability, but also improve the performance of MPCA based process monitoring as well as trend forecasting, which are of great significance to ensure safe and smooth running of batch processes. Finally, experimental studies consisting in fed-batch penicillin benchmark problems demonstrate the effectiveness and potentials of the contributions.