(375b) Designing Data-Based and Model-Based Methods for Process Monitoring and Equipment Degradation Tracking
Additionally, this work will demonstrate the necessity to track deviations in the process models themselves in order to identify process drifts or equipment degradation. When a process operates under control, a controller continuously adjusts the level of manipulated variables in order to ensure that the controlled variables are being maintained within specific limits. Unfortunately, this may be at the expense of increased operating and additional costs. Therefore, this work will utilize a dynamic contour-based algorithm in order to illustrate how equipment degradation can be efficiently tracked in multiple operating regimes .
For both algorithms, illustrative examples using simulated synthetic data, and real data from different applications will be utilized in order to highlight the effectiveness of the developed algorithms.
 I. T. Joliffe, Principal Component Analysis, 2nd ed. New York, NY: Springer-Verlag, 2002.
 M. R. Reynolds and J. Y. Lou, âAn Evaluation of a GLR Control Chart for Monitoring the Process Mean,â J. Qual. Technol., vol. 42, no. 3, pp. 287â310, 2010.
 M. Z. Sheriff, M. Mansouri, M. N. Karim, H. Nounou, and M. Nounou, âFault detection using multiscale PCA-based moving window GLRT,â J. Process Control, vol. 54, 2017, doi: 10.1016/j.jprocont.2017.03.004.
 M. Z. Sheriff, H. Nounou, M. Nounou, and M. N. Karim, âMonitoring process degradation through operating regime based process monitoring,â in AIChE Spring Meeting and Global Congress on Process Safety: Process Control Monitoring and Analytics, 2019.