Control charts in statistical process control are powerful tools for assessing the process stability. The traditional charts perform well under the assumption of normality, but when data violate the assumption of normality, the robust approaches are needed. The aim of the research is to develop exponentially weighted moving average control charts for mean (EWMAM) based on M-scale estimators with in-control variance under non-normal processes using repetitive sampling scheme. Since M-scale estimators are considered as an alternative to standard deviation, the control charts are developed using these estimators. The performance of proposed charts is compared with the traditional EWMAM chart on the basis of out-of-control average run lengths. The application on practical data set is also provided.
Robust Repetitive M-estimators Average run length EWMAM
This is a preview of subscription content, log in to check access.
The authors are thankful to the editor and reviewers for their valuable comments to improve the quality of this manuscript.
Ms. NS performed the research and Prof. Dr. SK supervised and contributed editorial input.
Compliance with Ethical Standards
Conflict of interest
The authors declare no conflict of interest regarding the publication of this paper.
Ahmad L, Aslam M, Jun CH (2014) Designing of X-bar control charts based on process capability index using repetitive sampling. Trans Inst Meas Control 36(3):367–374CrossRefGoogle Scholar
Aslam M, Azam M, Jun CH (2014a) New attributes and variables control charts under repetitive sampling. Ind Eng Manag Syst 13(1):101–106Google Scholar
Aslam M, Khan N, Azam M, Jun CH (2014b) Designing of a new monitoring T-chart using repetitive sampling. Inf Sci 269:210–216CrossRefGoogle Scholar
Azam M, Aslam M, Jun CH (2015) Designing of a hybrid exponentially weighted moving average control chart using repetitive sampling. Int J Adv Manuf Technol 77:1927–1933CrossRefGoogle Scholar
Azam M, Arif OH, Aslam M, Ejaz W (2016) Repetitive acceptance sampling plan based on exponentially weighted moving average regression estimator. J Comput Theor Nanosci 13(7):4413–4426CrossRefGoogle Scholar
Elamir E (2001) Probability distribution theory, generalizations and applications of L-moments. Dissertation, Durham UniversityGoogle Scholar
Figueiredo F, Gomes ML (2009) Monitoring industrial processes with robust control charts. Revstat Stat J 7(2):151–170MathSciNetGoogle Scholar
Lee H, Aslam M, Shakeel Q, Lee W, Jun C (2014) A control chart using an auxiliary variable and repetitive sampling for monitoring process mean. J Stat Comput Simul 85(16):3289–3296MathSciNetCrossRefGoogle Scholar
Montgomery DC (2001) Introduction to statistical quality control, 4th edn. Wiley, New YorkMATHGoogle Scholar
Qui P (2014) Introduction to statistical process control. CRC Press, LondonGoogle Scholar