Change point detection in process control with robust individuals control chart

Kooi Huat Ng,Kok Haur Ng,Jeng Young Liew

doi:10.1051/itmconf/20213601006

Abstract

It is crucial to realize when a process has changed and to what extent it has changed, then it would certainly ease the task. On occasion that practitioners could determine the time point of the change, they would have a smaller search window to pursue for the special cause. As a result, the special cause can be discovered quicker and the necessary actions to improve quality can be triggered sooner. In this paper, we had demonstrated the use of so-called exploratory data analysis robust modified individuals control chart incorporating the M-scale estimator and had made some comparisons to the existing charts. The proposed modified robust individuals control chart which incorporates the M-scale estimator in order to compute the process standard deviation offers substantial improvements over the existing median absolute deviation framework. With respect to the application in real data set, the proposed approach appears to perform better than the typical robust control chart, and outperforms other conventional charts particularly in the presence of contamination. Thus, it is for these reasons that the proposed modified robust individuals control chart is preferred especially when there is a possible existence of outliers in data collection process.

Highlights

It is crucial to realize when a process has changed and to what extent it has changed, it would certainly ease the task
The development of statistical process control charts has its purpose to aid practitioners to monitor if the change has developed [3]
In the event that if the inquirer is not intended to know whether the additional variation is due to between-groups variation, but merely to check if the extra variation is distributed randomly, the average moving range (AMR)-chart can be plotted

Summary

Introduction

The development of statistical process control charts has its purpose to aid practitioners to monitor if the change has developed [3]. As long as the control chart statistics fall within its limits, the control chart suggests that only the common causes of variation exist. When the chart statistics exceeds the limits, the control chart exhibits that there might be one or more special causes existed. [6] have discussed a stepwise approach to construct a robust Shewhart location control chart and [7] has done extensive work with frameworks of automating phase I process that affect the phase II performance for individuals control chart [4] had written widely about robust multivariate control chart in relation to goodness-of-fit test while [5] were investigating the concept of robust multivariate EWMA control chart concerning the sparse mean shifts. [6] have discussed a stepwise approach to construct a robust Shewhart location control chart and [7] has done extensive work with frameworks of automating phase I process that affect the phase II performance for individuals control chart

M-Scale estimate

The proposed control-charting procedure

Illustrative example: application to wood moisture content data

Findings

Conclusion