Imputation of Missing Data with Ordinary Kriging for Enhancing Fault Detection and Diagnosis

This work investigates the application of different metamodeling techniques for enhancing the information quality of the process history databases, through smoothing the noise/outliers and imputing missing data that usually contaminate such databases. The information quality enhancements are aimed at improving the training of the data-driven classification techniques used for Fault Detection and Diagnosis (FDD) of the process. A simulation case study of a Continuous Stirred Tank-Reactor (CSTR) is used to produce training datasets containing noisy, outlier and missing values. Three metamodeling techniques namely; Ordinary Kriging (OK), Artificial Neural Networks (ANN) and Polynomial Regression (PR) are used to smooth the noise and outliers, and to impute the missing values. Next, the FDD performance of the Support Vector Machines (SVM) classifier is compared when it trained with the recuperated datasets by the metamodels, while datasets have noisy, outlier and missing values. The results show high enhancement in the performance of the SVM when it trained with the recuperated data using the metamodels, especially when OK is exploited.