Smart Process Analytics for Process Monitoring

Abstract

Process monitoring is critical to ensuring product quality and efficient, safe process operation. Data-driven modeling is used in the process industries to build fault detection systems. No single data-driven modeling method provides the best fault detection performance for all process systems, and the selection of the best data-driven modeling method for a specific process system requires substantial expertise. In this study, we propose Smart Process Analytics for Process Monitoring (SPAfPM), a systematic framework for automatic method selection and calibration of data-driven fault detection models. A set of candidate methods is pre-selected from a library on the basis of the characteristics of the data. A rigorous cross-validation procedure is then employed to compare the models obtained by these methods to select the best data-driven model for fault detection. The performance of SPAfPM is demonstrated in four case studies, including the Tennessee Eastman Process.