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Monitoring dependent process steps using robust cause‐selecting control charts
Author(s) -
Asadzadeh Shervin,
Aghaie Abdollah,
Shahriari Hamid
Publication year - 2009
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1006
Subject(s) - outlier , robust regression , robust statistics , control chart , estimator , ordinary least squares , computer science , statistical process control , data mining , robust control , anomaly detection , process (computing) , control (management) , control limits , regression , robustness (evolution) , statistics , mathematics , engineering , machine learning , artificial intelligence , control system , biochemistry , chemistry , gene , electrical engineering , operating system
Cause‐selecting control charts are believed to be invaluable for monitoring and diagnosing multistage processes where the output quality of some stages is significantly impacted by the output quality of preceding stages. To establish a relationship between input and output variables, a standard procedure uses historical data, which are often prone to hold outliers. The presence of outliers tends to decrease the effectiveness of monitoring procedures because the regression model is distorted and the control limits become stretched. To dampen the negative repercussions of outliers, robust fitting techniques based on M ‐estimators are implemented instead of the ordinary least‐squares method and two robust monitoring approaches are presented. An example is given to illustrate the application and performance of the proposed control charts. Furthermore, a simulation‐based study is included to investigate and compare the average run length of robust and non‐robust schemes. The results reveal that the robust procedure far outperforms the non‐robust counterpart due to its prompt detection of out‐of‐control conditions when outliers exist. Copyright © 2009 John Wiley & Sons, Ltd.