Abstract
The authors demonstrate the application of traditional process monitoring and control methods such as parameter estimation, alongside Univariate Statistical Process Control techniques that have traditionally been used for quality control and monitoring. Further work is also presented on the use of Multivariate Statistical Process Control methods for process monitoring and improvement. A restriction on this work is that only the data currently used by the process monitoring and control system is considered. This is a realistic constraint, as many companies will not be able to provide a bank of additional sensors for the purposes of fault detection. This paper is of interest to researchers and practitioners alike whose ultimate aim is to apply process monitoring, fault detection and isolation in 'real world" contexts rather than in the confmes of the laboratory. It provides a useful step forward in the provision of guidelines for the selection of techniques for particular process types and desired outcomes.
Published Version
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