Madakyaru, Muddu and Harrou, Fouzi and Sun, Ying (2017) Improved data-based fault detection strategy andapplication to distillation columns. Process Safety and Environmental Protection: Transactions of the Institution of Chemical Engineers, Part B, 107. pp. 22-34. ISSN 0957-5820

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Abstract

tChemical and petrochemical processes require continuous monitoring to detect abnor-mal events and to sustain normal operations. Furthermore, process monitoring enhancesproductivity, efficiency, and safety in process industries. Here, we propose an innova-tive statistical approach that exploits the advantages of multiscale partial least squares(MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes.Specifically, we combine an MSPLS algorithm with wavelet analysis to create our model-ing framework. Then, we use GLR hypothesis testing based on the uncorrelated residualsobtained from the MSPLS model to improve fault detection. We use simulated distillationcolumn data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-basedGLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Qmethod, especially in early detection of small faults with abrupt or incipient behavior

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