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Cause-selecting control charts based on Huber’s M-estimator

Cause-selecting control charts based on Huber’s M-estimator Cause-selecting chart (CSC) is effective in monitoring and diagnosing multistage processes. It discriminates between the overall and specific qualities by establishing the relationship between input and output measurements. In practice, the model relating the input and output variables must be estimated. To this end, historical data are used, which often contain outliers. The presence of outliers has a deleterious effect on the control charting procedure. To alleviate the encountered problem, a robust monitoring approach based on Huber’s M-estimator is proposed. Subsequently, the performance of the robust and non-robust CSCs is investigated using the average run length criterion while conducting a simulation study. The results reveal that the Huber-based CSC is superior to the traditional CSC due to its prompt detection of out-of-control conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Cause-selecting control charts based on Huber’s M-estimator

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer-Verlag London Limited
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
DOI
10.1007/s00170-009-1966-2
Publisher site
See Article on Publisher Site

Abstract

Cause-selecting chart (CSC) is effective in monitoring and diagnosing multistage processes. It discriminates between the overall and specific qualities by establishing the relationship between input and output measurements. In practice, the model relating the input and output variables must be estimated. To this end, historical data are used, which often contain outliers. The presence of outliers has a deleterious effect on the control charting procedure. To alleviate the encountered problem, a robust monitoring approach based on Huber’s M-estimator is proposed. Subsequently, the performance of the robust and non-robust CSCs is investigated using the average run length criterion while conducting a simulation study. The results reveal that the Huber-based CSC is superior to the traditional CSC due to its prompt detection of out-of-control conditions.

Journal

The International Journal of Advanced Manufacturing Technology – Springer Journals

Published: Mar 13, 2009

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