Abstract

Control charts for monitoring linear profiles are used to control quality processes which are characterized by a relationship between a response variable and one or more explanatory variables. In the literature, the majority of control charts deal with phase II analysis of linear profiles, where the objective is to assess the performance of control charts in detecting shifts in the parameters of linear profiles. Recently, the kernel distance-based multivariate control chart, also known as the K-chart, has received much attention as a promising nonparametric control chart with high sensitivity to small shifts in the process. Despite its numerous advantages, no work has proposed the use of the K-chart for monitoring simple linear profiles and that serves the motivation for this paper. This paper proposes the use of the K-chart for monitoring simple linear profiles. A benchmark example is used to show the construction methodology of the K-chart for simultaneously monitoring the slope and intercept of linear profile. In addition, performance of the K-chart in detecting out-of-control profiles is assessed and compared with traditional control charts. Results demonstrate that the K-chart performs better than the T2 control chart, EWMA control chart, and R-chart under small shift in the slope.

Highlights

  • In the last decade, control charts for monitoring linear profiles have acquired a prominent role in controlling quality processes characterized by a relationship between a response variable and one or more explanatory variables

  • Control charts for monitoring linear profiles are used to control quality processes which are characterized by a relationship between a response variable and one or more explanatory variables

  • Zou et al [3] proposed a multivariate EWMA scheme when the quality process is characterized by a general linear profile

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Summary

Introduction

Control charts for monitoring linear profiles have acquired a prominent role in controlling quality processes characterized by a relationship between a response variable and one or more explanatory variables. According to Gani et al [8], the K-chart gives the minimum volume closed spherical boundary around the in-control process data. It measures the distance between the kernel center and the incoming new sample to be monitored, which can be calculated using support vectors (SVs). A benchmark simulated data is used in Section 4 to illustrate the application of Kchart for simultaneously monitoring the slope and intercept of linear profiles, with a comparison with traditional control charts. Phase II monitoring of linear profiles remains the most important step since it aims to assess the performance of control charts in detecting shifts in the parameters of linear profiles. We present the main control charts for phase II analysis of linear profiles

Monitoring Simple Linear Profiles
Monitoring Simple Linear Profiles Using the K-Chart
Application
Performance Assessment
Conclusion
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