Browsing by Author "Addo, Prince"
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- ItemAdaptive process monitoring using principal component analysis and Gaussian Mixture Models(Stellenbosch : Stellenbosch University, 2019-04) Addo, Prince; Auret, Lidia; Kroon, R. S. (Steve); Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.ENGLISH ABSTRACT: Principal component analysis (PCA) is a well-known technique used in combination with monitoring statistics for fault detection. Moving window PCA and recursive PCA are adaptive extensions of PCA that operate by periodically updating the monitoring model to incorporate new observations. This allows the monitoring model to cope with process behaviours that change slowly over time such as equipment aging, catalyst deactivation, and reaction kinetics drift and thereby improving monitoring performance. Recent demands and advancements in process industries, however, may result in multimodal operations, where distinct clusters are present in measurement data. The performance of the aforementioned PCA-based monitoring techniques is hindered due to the violation of the implicit assumption that all the observed process data belong to the same Gaussian distribution. To improve monitoring performance, multimodal techniques are required. The Gaussian mixture model (GMM) is a probabilistic model that can account for the observed modes in the process data and therefore be used in the monitoring of multimode processes. However, multimodal processes also exhibit behaviours that change slowly over time, which is challenging. This work develops a monitoring approach that extends adaptive PCA techniques to GMM, which effectively addresses the aforementioned challenge. This is done by continuously refreshing the model parameters and monitoring statistics for the PCA and GMM. Other key areas that the work focuses on are in improving the specifications for adaptive PCA protocol (taking into consideration the various model update methods) and Gaussian mixture model methods (taking into consideration the monitoring model types and data types). Also, the performance of unimodal and multimodal process monitoring approaches was assessed. The performance of the developed approach and the improved implementations of the pre-existing methods were assessed using various case studies including unimodal and multimodal processes both with and without drift as well as various fault types. The Tennessee Eastman process and the non-isothermal continuously stirred tank reactor process are the two main simulators considered. Results for the considered cases show improved performance for the developed approach (adaptive PCA-based GMM) as compared to PCA, adaptive PCA, and traditional GMM, in fault detection. The GMM, as expected, performed better for multimodal cases than the PCA approaches. Also, the adaptive PCA approach performed better than PCA when there is process drift.