Principal component analysis of production data

This paper develops the use of principal component analysis as an aid to quality assurance. The sensitivity of the analysis to changes in the statistical characteristics of the measurements is examined. The analysis can reduce the number of time series that have to be monitored for quality assurance. The technique is to linearly combine groups of the measurements. This grouping of measurements excludes the use of the cusum as a quality assurance aid. A new quality monitoring process is proposed; the cumoderror, which enables the detection of mean level and variance changes in single or combined measurements.