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Error Detection and Pattern Prediction Through Phase II Process Monitoring

Azam Zaka1, Riffat Jabeen2,*, Kanwal Iqbal Khan3

1 Government Graduate College of Science, Wahdat Road, Lahore, Pakistan
2 COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
3 Institute of Business & Management, University of Engineering and Technology, Lahore, Pakistan

* Corresponding Author: Riffat Jabeen. Email: email

(This article belongs to this Special Issue: Emerging Trends and Real-World Applications of Intelligent Computing Techniques)

Computers, Materials & Continua 2022, 70(3), 4781-4802. https://doi.org/10.32604/cmc.2022.020316

Abstract

The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The findings suggest that an extended exponentially weighted moving average control chart based on the percentiles estimator performs better than exponentially weighted moving average control charts based on the percentiles estimator and modified maximum likelihood estimator. Further, these results will help the firms in the early detection of errors that enhance the process reliability of the telecommunications and financing industry.

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Cite This Article

A. Zaka, R. Jabeen and K. Iqbal Khan, "Error detection and pattern prediction through phase ii process monitoring," Computers, Materials & Continua, vol. 70, no.3, pp. 4781–4802, 2022. https://doi.org/10.32604/cmc.2022.020316

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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