Articlegreen2019

Guaranteed in-control control chart performance with cautious parameter learning

37 citations0/0 files storedJournal of Quality Technology

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Management Systems and Quality Improvement

Abstract

Parameter estimation has a large impact on control chart performance. Recently, widened control limits have been suggested to guarantee an acceptable in-control behavior. However, the consequence is a reduced ability to detect a real change in the process. In order to overcome this undesired effect, we explore an alternative design based on a delayed updating of parameter estimates. We consider an application to the Shewhart X, EWMA, and CUSUM control charts for the process mean. This approach is simple to implement, reduces the variation of the in-control average run lengths, and significantly improves the out-of-control performance.

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References

2003Stochastic Approximation and Recursive Algorithms and Applications 1992Acceleration of Stochastic Approximation by Averaging 2006Effects of Parameter Estimation on Control Chart Properties: A Literature Review 1987Introduction to Statistical Quality Control 2013Introduction to Statistical Process Control 1991SPCQCharts for Start-Up Processes and Short or Long Runs 2014An Overview of Phase I Analysis for Process Improvement and Monitoring 1987Self-Starting Cusum Charts for Location and Scale

Cited by

2021Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs 2023A CUSUM control chart for gamma distribution with guaranteed performance 2022On designing an optimal SPRT control chart with estimated process parameters under guaranteed in-control performance 2022Improved control chart performance using cautious parameter learning 2024Navigating Process Drift: The Power of CUSUM in Monitoring Air Quality Processes and Maintenance Operations

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