Home Probability Weighted Moments Approach to Quality Control Charts
Article
Licensed
Unlicensed Requires Authentication

Probability Weighted Moments Approach to Quality Control Charts

  • Faqir Muhammad and Muhammad Riaz
Published/Copyright: March 10, 2010
Become an author with De Gruyter Brill
Stochastics and Quality Control
From the journal Volume 21 Issue 2

Abstract

A new control chart namely Spw-Chart for monitoring the changes in the process variability is proposed and is based on Probability Weighted Moments (PWMs) and assuming that the quality characteristic follows a normal distribution. The coefficients r2 and r3 (similar as the d2 and d3 coefficients used for R-Charts) are derived for sample sizes n = 2, 3, . . . , 20, 25, 30, 35, 50, 100 by means of a simulation study. The quantiles of which are used for determining the values of the control limits and the power of the Spw-Chart to detect shifts in process variability, are also derived for n = 2, 3, . . . , 20, 25, 30, 35, 50, 100 by simulation. Each of the simulation studies is based on 10,000 random samples from the corresponding normal distribution. The performance of Spw-Chart is investigated by comparing its power curves with those of R and S Charts. It is observed that the power curves of the Spw-Chart are above those of the R-Chart, while slightly below those of the S-Chart in detecting shifts in the process variability. The effect of non-normality on the designs of S, R, and Spw Charts, is studied by simulating random samples from the exponential and the t distributions. The simulations reveal superiority of the Spw-Chart over both R and S Charts in the sense that the power curve of Spw-Chart is least affected by non-normality among all the three charts under study.

Published Online: 2010-03-10
Published in Print: 2006-October

© Heldermann Verlag

Downloaded on 23.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/EQC.2006.251/html
Scroll to top button