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Statistical Monitoring of Complex Multivariate Processes: With Applications in Industrial Process Control by Lei Xie, Uwe Kruger

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Chapter 1

Motivation for multivariate statistical process control

This first chapter outlines the basic principles of multivariate statistical process control. For the reader unfamiliar with statistical-based process monitoring, a brief revision of statistical process control (SPC) and its application to industrial process monitoring are provided in Section 1.1.

The required extension to MSPC to address data correlation is then motivated in Section 1.2. This section also highlights the need to extract relevant information from a large dimensional data space, that is the space in which the variation of recorded variables is described. The extracted information is described in a reduced dimensional data space that is a subspace of the original data space.

To help readers unfamiliar with MSPC technology, Section 1.3 offers a tutorial session, which includes a number of questions, small calculations/examples and projects to help familiarization with the subject and to enhance the learning outcomes. The answers to these questions can be found in this chapter. Project 2 to 4 require some self study and result in a detailed understanding on how to interpret SPC monitoring charts for detecting incipient fault conditions.

1.1 Summary of statistical process control

Statistical process control has been introduced into general manufacturing industry for monitoring process performance and product quality, and to observe the general process variation, exhibited in a few key process variables. ...

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