• A. B. Zoramawa
  • S. Suleiman
  • A. M. Dogondaji
  • S. D. Suru
Keywords: Control Charts, EWMA, Inverse weibull distribution, Moving average, average rerun


Statistical control charts are widely used in industry for process and measurement control to monitor production processes in order to discover any problem or issues that may arise during the production process and to help in finding solutions for these issues. This research proposes the exponentially weighted moving average (EWMA) control chart, comparing it’s performance to NP and MA control chart for monitoring the number of nonconforming with the truncated lifetime of a product when the lifetimes of products follows  inverse Weibull (IW) distribution. The number of failures during the life test is used as an indicator of the quality of the product. EWMA control chart was proposed for this specific situation, thus extending the applicability of control charts methodology to situations involving truncated life tests. The performance of these control charts was measured with the average run length (ARL). The simulation shows that the results obtained from the EWMA chart using  IW distribution is more sensitive in detecting small shift and performs much better to monitoring shifts as when compared to other control charts considered in the study.


Abbas, N., Riaz, M., Does, R.J.M.M., (2012). Mixed Exponentially Weighted Moving Average—Cumulative Sum Charts for Process Monitoring. Quality and Reliability Engineering International,29(3):345–56.

Aslam, M., S. Balamurali, C.-H. Jun, and Ahmad, M. (2010). A two-plan sampling system for life testing under Weibull distribution.Industrial Engineering and Management Systems,9: 54–59. http://dx.doi. org/10.7232/iems.2010.9.1.054

Calabria, R. and Pulcini, G. (1989). Confidence limits for reliability and tolerance limits in the inverse Weibull distribution. Engineering and System Safety, 24, 77-85.

Calabria, R. and Pulcini, G. (1990). On the maximum likelihood and least squares estimation in inverse Weibull distribution, Statistical Application, 2, 53-66.

Calabria, R. and Pulcini, G. (1994). Bayes 2-sample prediction for the inverse Weibull distribution.Communications in Statistics-Theory and Methods, 23(6), 1811-1824

Carson, P.K. and Yeh, A.B. (2008). Exponentially weighted moving average (EWMA) control charts for monitoring an analytical process. Ind Eng Chem Res ;47:405–11. https://doi.org/10.1021/ie070589b.

Chananet, C., Sukparungsee, S. and Areepong, Y. (2014).The ARL of EWMA chart for monitoring ZINB model using Markov chain approach. Int. J. Appl. Phys. Math., vol. 4, p. 236.

How to Cite
Zoramawa A. B., Suleiman S., Dogondaji A. M., & Suru S. D. (2023). ON EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHART FOR TIME TRUNCATED LIFE TEST USING INVERSE WEIBULL DISTRIBUTION. FUDMA JOURNAL OF SCIENCES, 7(5), 221 - 227. https://doi.org/10.33003/fjs-2023-0705-2015