On a class of mixed EWMA‐CUSUM median control charts for process monitoring

Abstract

Monitoring of any manufacturing, production, or industrial process can be controlled and improved by removing these special cause of variations using control charts. Shewhart‐type control charts are effective to control a large amount of special variations, whereas, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) charts detect small and moderate variations efficiently in the process parameters. Monitoring of location parameter can be done with mean control charts under the assumption that the parameters are known or correctly estimated from in‐control samples and data are free from outliers (but in practice data occasionally have outliers). In this study, we have proposed generalized mixed EWMA‐CUSUM median control charts structures for known and unknown parameters based on auxiliary variables for detecting shifts in process location parameter. The proposed charts are compared with the corresponding charts for the mean, based on contaminated and uncontaminated data. Different performance measures are used to evaluate the performance of proposed control charts and revealed through results that the median‐based charts are more sensitive to detect a shift in process location parameter in the presence of outliers. An illustrative example using real data is also shown for practical consideration.

A new EWMA median chart with measurement errors in short production runs–An application in yogurt cup filling process

“…proposed the mean and median based CUSUM charts for monitoring censored data for Weibull lifetime. Other researches on median charts were made by Ahmad et al., 8 Tran et al., 9 Hu and Castagliola, 10 Tang et al., 11 Khoo et al., 12 Saha et al., 13 Hussain et al, 14 and Khoo et al 15 …”

“…Raza et al 7 proposed the mean and median based CUSUM charts for monitoring censored data for Weibull lifetime. Other researches on median charts were made by Ahmad et al, 8 Tran et al, 9 Hu and Castagliola, 10 Tang et al, 11 Khoo et al, 12 Saha et al, 13 Hussain et al, 14 and Khoo et al 15 On a different note, SPR charts have also received the attention of many researchers. The traditional manufacturing environment involves mass production with an unlimited production horizon.…”

Economic and economic‐statistical designs of the side sensitive group runs chart with auxiliary information

“…More recently, the AI concept was adopted in more complex control charts. Saha et al 14 incorporated the AI concept into the SSGR (denoted as SSGR‐AI) chart which outperforms the classical SSGR chart of Gadre and Rattihalli, 8 while Abbasi et al, 15 Haq and Abidin, 16 Amir et al, 17 Ng et al, 18 and Hussain et al 19 proposed the AI‐based EWMA variability, CUSUM t , moving average, run sum and mixed EWMA‐CUSUM median charts, respectively.…”

Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing

“…Among the strategies forecasting the future value in the moving average time series, the EWMA method is efficiently minimises the mean square error. In recent years, many kinds of literature have studied how to use EWMA controllers to form control charts to monitor future data [10,11,17,18]. To monitor carbon emissions, Shamsuzzaman et al integrated a Shewhart control chart with an EWMA scheme to reduce costs [19].…”