Improved Data-based Fault Detection Strategy and Application to Distillation Columns
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2017-01-31
Print Publication Date2017-04
Permanent link to this recordhttp://hdl.handle.net/10754/622854
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AbstractChemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative 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 modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.
CitationMadakyaru M, Harrou F, Sun Y (2017) Improved Data-based Fault Detection Strategy and Application to Distillation Columns. Process Safety and Environmental Protection. Available: http://dx.doi.org/10.1016/j.psep.2017.01.017.
SponsorsThe work reported in this paper was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.