Adaptive EWMA procedures for monitoring processes subject to linear drifts

Authors:
Yan Su;Lianjie Shu;Kwok-Leung Tsui
Affiliations:
Department of Electromechanical Engineering, University of Macau, Macau;Faculty of Business Administration, University of Macau, Taipa, Macau;School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States
Venue:
Computational Statistics & Data Analysis
Year:
2011

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Abstract

The conventional Statistical Process Control (SPC) techniques have been focused mostly on the detection of step changes in process means. However, there are often settings for monitoring linear drifts in process means, e.g., the gradual change due to tool wear or similar causes. The adaptive exponentially weighted moving average (AEWMA) procedures proposed by Yashchin (1995) have received a great deal of attention mainly for estimating and monitoring step mean shifts. This paper analyzes the performance of AEWMA schemes in signaling linear drifts. A numerical procedure based on the integral equation approach is presented for computing the average run length (ARL) of AEWMA charts under linear drifts in the mean. The comparison results favor the AEWMA chart under linear drifts. Some guidelines for designing AEWMA charts for detecting linear drifts are presented.