Abstract
In most quality control practices, the mean control chart is used in conjunction with the dispersion control chart to maintain the stability of a production process. Existing literature has suggested setting the in-control average run length (ARL0) of the mean chart and the dispersion chart to be equal. While such designs have the advantage of convenience, it is often unrealistic to assume that the mean and dispersion charts will share a common sensitivity, since mean shifts and variance shifts may occur with different probabilities in practice. In this paper, we propose a mismatched in-control performances strategy for designing the joint sequential probability ratio test (SPRT) mean and dispersion chart. We also develop new optimization designs for the joint SPRT scheme. Results show that the mismatched in-control performances strategy effectively boosts the detection sensitivity of the joint SPRT scheme towards mean shifts alone or variance shifts alone. It is also revealed that the mismatched joint SPRT scheme with a larger ARL on the dispersion chart performs better than the traditional matched joint SPRT scheme for a wide range of process shifts.
Original language | English |
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Title of host publication | 20th IEEE International Colloquium on Signal Processing and Its Applications 2024 |
Publisher | IEEE |
Pages | 12-17 |
Number of pages | 6 |
ISBN (Electronic) | 9798350382310 |
DOIs | |
Publication status | Published - 14 May 2024 |
Event | 20th IEEE International Colloquium on Signal Processing and Its Applications 2024 - Langkawi, Malaysia Duration: 1 Mar 2024 → 2 Mar 2024 Conference number: 20 https://www.aconf.org/conf_194153.html |
Conference
Conference | 20th IEEE International Colloquium on Signal Processing and Its Applications 2024 |
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Abbreviated title | CSPA 2024 |
Country/Territory | Malaysia |
City | Langkawi |
Period | 1/03/24 → 2/03/24 |
Internet address |
Keywords
- average run length
- joint monitoring control chart
- manufacturing engineering
- sequential probability ratio test
- statistical quality control
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Signal Processing
- Media Technology
- Control and Optimization
- Modelling and Simulation