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PCA - Partial Component Analysis
Introduction
Statistical process control, SPC, is being implemented across the industry without
consideration to the multivariable nature of the measurements. The objective of this
chapter is to provide basic statistical theory and application of the Principal Component
Analysis (PCA) technique to evaluate and understand process unit data. The theory is
presented from the user’s point of view and more detail of mathematical derivations can
be found in the references. The technology is mainly being introduced to the chemical
industry by John MacGregor of McMaster University.
The statistical process control, SPC, and statistical quality control, SQC, foundations are
based upon the detection of a statistically significant change in a given process. When a
process can be described by a single measurement, such as making an object with a
desired length, implementation of these techniques becomes an easy task.
However in the majority of the industries, a given process such as a reactor can not be
described completely by a single measurement. Implementation of SQC and SPC
technology will yield hundreds of control charts which will identify some causes but not
the status of the process.
The multivariable nature of processes makes it difficult to provide the unit operator with
the information with respect to the health of the process based on SQC and SPC
technology due to the number of charts generated.
The PCA technique presented in this chapter will reduce a large multivariable system
(i.e. 10 tags with 200 measurements) to a manageable system (i.e. 2 tags with 200
measurement) while preserving most of the information in the original system.
Nested SQC Charts
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