ABSTRACT
In this chapter, the issues associated with monitoring the operational status of a process are discussed. This discussion is placed in the context of statistical process control (SPC) and statistical techniques to quantify the in-control behavior of a process are introduced. The goal is to present a number of statistical concepts to monitor the performance of a process over time, with emphasis on the detection of abnormal events, which may ultimately lead to degradation, or violation of the product quality specifications. The detection and further isolation of these events help allocation of a cause, which, if eliminated, results in improved reliability and control of the final product. The control charts are introduced, along with projection techniques using latent variables. Furthermore, as part of continued interest in Big Data, data reduction and classification using modern projection and clustering tools are discussed. The fault diagnosis and classification techniques are supplemented with controller performance monitoring (CPM) techniques to identify and correct operational problems that can be attributed to the control system. A stochastic measure that uses the minimum variance controller as the benchmark is offered as a means to evaluate how far the tuned controller may be from the ideal performance.
