Parametric control charts, such as Shewhart chart, Cumulative Sum(CUSUM) chart, Exponentially Weighted Moving Average (EWMA) chart, and their extensions, have been proven to perform satisfactory in many situations.However, they are often constructed based on the assumption that the underlying processfollowsnormal (or multi-normal, for multivariate control charts) distribution. The performance of parametric control charts could be seriously affected if
the normal assumption is violated, despite the effect of central limit theorem. Inthisresearch, several distribution-freenonparametriccontrol chartsare proposed.
The proposed control charts do not relyon normal assumption, and they can be used when the underlying process distribution is not well known. The nonparametric control charts are developed to address some major topics in statisti-
cal process control (SPC), such as monitoring process mean, monitoring process variance, Phase I (retrospective) analysis of historical data sample, and monitoring linear profiles. The nonparametric methods are often lessfavorable compared
to parametric control charts, due to their lower power-of-the-test. However, it is shown in the dissertation that, our proposed nonparametric control charts perform quit close to their parametric counterparts, if the process parameters areconsidered being estimated from reference sample.
The exact run-length distributions of the proposed control charts are derived, the average run-length (ARL) properties are investigated, and several numerical examples are presented for illustration purpose. It has been found, parametric control charts generally have too short in-control ARLs under non-normal distributions, and the proposed nonparametric control charts perform consistentlyin terms of in-control ARL under all distribution scenarios. A notable improvement of the proposed nonparametric control charts, over existing nonparametriccontrol charts, is that they are still sensitive under normal distribution. Therefore, they can be used in place of the traditional parametric control charts without losing much power.
Name: Li, Suyi, 1980-
Title: Nonparametric distribution-free control charts based on rank statistics / Suyi Li.
Description: Singapore : Encyclopaedic Publishing Pte. Ltd., [2020] | Includes bibliographic references.
Identifier(s): OCN 1130396650 | ISBN 978-981-14-4070-0 (paperback)
Subject(s): LCSH: Quality control--Statistical methods. | Process control--Statistical methods.
Classification: DDC 620.00450151--dc23