Open Access

ARTICLE

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

by Wimonmas Bamrungsetthapong¹, Pramote Charongrattanasakul^2,*

1 Division of Applied Statistics, Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, Pathum Thani, 12110, Thailand
2 Division of Mathematics, Department of Mathematics and Computer Science, Faculty of Science and Technology, Rajamangala University of Technology Krungthep, Bangkok, 10120, Thailand

* Corresponding Author: Pramote Charongrattanasakul. Email:

Computer Modeling in Engineering & Sciences 2024, 138(3), 2471-2495. https://doi.org/10.32604/cmes.2023.031433

Received 15 June 2023; Accepted 11 September 2023; Issue published 15 December 2023

Abstract

This study presents the design of a modified attributed control chart based on a double sampling (DS) np chart applied in combination with generalized multiple dependent state (GMDS) sampling to monitor the mean life of the product based on the time truncated life test employing the Weibull distribution. The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing. Three control limit levels are used: the warning control limit, inner control limit, and outer control limit. Together, they enhance the capability for variation detection. A genetic algorithm can be used for optimization during the in-control process, whereby the optimal parameters can be established for the proposed control chart. The control chart performance is assessed using the average run length, while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design with multiple linear regression. A comparative study was conducted based on the out-of-control average run length, in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts. Finally, to exhibit the utility of the developed control chart, this paper presents its application using simulated data with parameters drawn from the real set of data.

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