An Application in Multivariate Statistical Process Control for Power Supply Calibration


Mader, Douglas P.; Glycenfer, John J.; Prins, Jack   (1996, Marcel Dekker, Inc. and ASQC)   Quality Assurance Department, Hewlett-Packard Company, Greeley, CO; Test Engineering Department, Advanced Energy Industries, Fort Collins, CO; Worldwide Enterprise Modeling Group, Motorola Manufacturing Systems, Schaumburg, IL;

Quality Engineering    Vol. 9    No. 1
QICID: 15093    September 1996    pp. 99-106
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Article Abstract

[This abstract is based on the author's abstract.]

An application in real-time multivariate statistical process control for the automated power calibration of a family of high-energy power supplies is described. After a variance-stabilizing transformation, data for the actual power produced by each unit being tested is regressed on the specified input power over the operating range. The slope and intercept from the simple linear regression are compared to bivariate normal distribution for slope-intercept pairs from previous units using a prediction approach based on multivariate multiple linear regression. The approach has effectively identified quality problems on-line, and the efficiency of the multivariate SPC procedure has reduced the total test cycle time at the automated test. Process capability results for the power calibration test are given for the first 632 production units for the Lightning power supply family. Results strongly indicate that a six-sigma level of quality has been achieved for the power calibration process.


Calibration,Hotelling's T2 statistic,Linear regression,Statistical process control (SPC),Normal distribution,Prediction,Six Sigma,Multivariate quality control

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