Designing a multivariate exponentially weighted moving average control chart with measurement errors

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Shahed University, Tehran, Iran.

2 Department of Industrial Engineering, Shahed University, Tehran, Iran

10.22070/jqepo.2021.5667.1163

Abstract

Control chart is one of the useful tools of statistical process control, which monitor the processes over time. In most of the designed control charts, it is assumed that the measurement errors do not exist in the measurement system, while this assumption is usually violated in practice as well. The presence of measurement errors leads to poor performance of the control charts. In this paper, a multivariate exponentially weighted moving average control chart is designed by considering measurement errors in Phase II. To decline the effect of measurement errors on the performance of the proposed control chart, multiple measurements method is applied. Also, sensitivity analysis about the effect of the number of measurements on the ARL performance of the proposed control chart is conducted. Note that different scenarios for the variance-covariance matrix are considered in simulation studies, including Case 1. Uncorrelated case with equal variances. Case 2. Negatively correlated case with equal variances. Case 3. Uncorrelated case with unequal variances. Case 4. Positively correlated case with unequal variances. Moreover, the performance of the proposed control chart is compared with the performance of Hotelling's T2 control chart. Results show the admissible performance of the proposed method in decreasing the effect of measurement errors.

Keywords


Abbasi, S. A. (2010). On the performance of EWMA chart in the presence of two-component measurement error. Quality Engineering, 22(3): 199-213.
Amiri, A., Ghashghaei, R.,  Maleki, M. R. (2018). On the effect of measurement errors in simultaneous monitoring of mean vector and covariance matrix of multivariate processes. Transactions of the Institute of Measurement and Control40(1), 318–330. 
Cocchi, D. & Scagliarini, M. (2007). Control charts and the effect of the two-component measurement error model. Working Paper. Dipartimento di Scienze Statistiche ‘‘Paolo Fortunati,’’ Alma Mater Studiorum Universit di Bologna, Bologna, IT.
Ding, G. & L. Zeng (2015). On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes. Journal of Manufacturing Systems, 36: 263-273.
Ghashghaei, R., Bashiri, M., Amiri, A.,Maleki, M. R. (2016). Effect of measurement error on joint monitoring of process mean and variability under ranked set sampling. Quality and Reliability Engineering International32(8): 3035-3050.
Linna, K. W. & W. H. Woodall (2001). Effect of measurement error on Shewhart control charts. Journal of Quality Technology, 33(2): 213-222.
Linna, K. W., Woodall, W. H., & Busby, K. L. (2001). The performance of multivariate control charts in the presence of measurement error. Journal of Quality Technology33(3), 349-355.
Maleki, M. R., Amiri, A., Castagliola, P. (2017). Measurement errors in statistical process monitoring: a literature review. Computers & Industrial Engineering103, 316-329.
Maravelakis, P., Panaretos, J., Psarakis, S. (2004). EWMA chart and measurement error. Journal of Applied Statistics31(4), 445-455.
Maravelakis, P. E. (2012). Measurement error effect on the CUSUM control chart. Journal of Applied Statistics, 39(2): 323-336.
Du Nguyen, H., Nguyen, Q. T., Nguyen, T. H., Balakrishnan, N., & Tran, K. P. (2020). The Performance of the EWMA Median Chart in the Presence of Measurement Error.  Artificial Intelligence Evolution, 1(1), 48-62.
Noorossana, R. & Y. Zerehsaz (2015). Effect of measurement error on phase II monitoring of simple linear profiles. The International Journal of Advanced Manufacturing Technology, 79(9): 2031-2040.
Noor-ul-Amin, M. (2020). Impact of Measurement Error on Mixed EWMA-CUSUM Control Chart. Published online in Scientia Iranica. Doi:10.24200/SCI.2020.53453.3244.
Sabahno, H., Castagliola, P., Amiri, A. (2020a). A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors. Quality and Reliability Engineering International36(4), 1161-1196.
Sabahno, H., Castagliola, P., Amiri, A. (2020b). An adaptive variable-parameters scheme for the simultaneous monitoring of the mean and variability of an autocorrelated multivariate normal process. Journal of Statistical Computation and Simulation, 90(8) 1-36.
Sabahno, H., Amiri, A.,  Castagliola, P. (2019a). Optimal performance of the variable sample sizes Hotelling’s T2 control chart in the presence of measurement errors. Quality Technology & Quantitative Management, 16(5): 588-612.
Sabahno, H., Amiri, A. ,Castagliola, P. (2019b). Performance of the variable parameters  control chart in presence of measurement errors. Journal of Testing and Evaluation, 47(1): 480-497.
Sabahno, H., Amiri, A., Castagliola, P. (2018). Evaluating the effect of measurement errors on the performance of the variable sampling intervals Hotelling's T2 control charts. Quality and Reliability Engineering International, 34 (8): 1785-1799.
Tran KP, Castagliola P, Celano G. (2015) Monitoring the ratio of two normal variables using EWMA type control charts. Quality and Reliability Engineering International, 32(5):1853-1869.
Zaidi, F. S., Castagliola, P., Tran, K. P., & Khoo, M. B. C. (2020). Performance of the MEWMA‐CoDa control chart in the presence of measurement errors. Quality and Reliability Engineering International36(7), 2411-2440.