Fault Detection Using Principal Component Analysis (pca) in a Wastewater Treatment Plant (wwtp)
نویسنده
چکیده
In this paper Principal Components Analysis (PCA) is used for detecting faults in a simulated wastewater treatment plant (WWTP). PCA is a multivariate statistical technique used in multivariate statistical process control (MSPC) and fault detection and isolation (FDI) perspectives. PCA reduces the dimensionality of the original historical data by projecting it onto a lower dimensionality space. It obtains the principal causes of variability in a process. If some of these causes changes, it can be due to a fault in the process. False detected alarms due to measured disturbances are treated using Switch-PCA.
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