Monitoring of coefficient of variation with function‐based adaptive exponentially weighted moving average control chart

Abstract

In this paper, a new adaptive control chart is presented to study the relative process variability, which is called the coefficient of variation (CV). The CV study is the monitoring of a process with an unstable mean value over time. So, a function-based adaptive exponentially weighted moving average (FAEWMA) CV control chart is proposed to detect the infrequent changes in the process CV, named as "FAEWMA CV" control chart. The function is suggested to adapt the smoothing constant (SC) of the plotting statistic as per arising process shift size in CV. The efficiency is examined through the run-length values, which are computed through the Monte Carlo (MC) simulation method. The FAEWMA CV chart outperformed the existing AEWMA CV charts. A real-life example is studied to explain implementation details in the example section.

A near‐optimal CUSUM‐CV chart

“…For CV charts that do not fall in any of these categories, they are classified under the “others” category. The EWMA‐based CV charts were proposed by Hong et al., 2,3 Castagliola et al., 4 Zhang et al., 5 Yeong et al., 6 Muhammad et al., 7 Zhang et al., 8 Haq and Khoo, 9 Teoh et al., 10 Arshad et al., 11 and Hu et al 12 . CV charts that are based on the CUSUM approach were suggested by Lee et al., 13,14 Tran and Heuchenne, 15 Tran et al., 16 and Tran and Heuchenne 17 .…”

Adaptive EWMA control chart for monitoring the coefficient of variation under ranked set sampling schemes

“…Tran and Heuchenne 28 presented CV control chart by using the variable sampling based CUSUM control chart. Arshad et al 29 suggested a function based CV control chart for monitoring the shifts in CV.…”