Performance and Robustness of Control Charting Methods for Autocorrelated Data
- Resource Type
- Academic Journal
- Authors
- Chang-Ho Chin; Daniel W. Apley
- Source
- 대한산업공학회지. 2008-06 34(2):122-139
- Subject
- Control Charts
Autocorrelation
Robustness
Average Run Length
Sensitivity Measure
- Language
- Korean
- ISSN
- 1225-0988
2234-6457
With the proliferation of in-process measurement technology, autocorrelated data are increasingly common in industrial SPC applications. A number of high performance control charting techniques that take into account the specific characteristics of the autocorrelation through time series modeling have been proposed over the past decade. We present a survey of such methods and analyze and compare their performances for a range of typical autocorrelated process models. One practical concern with these methods is that their performances are often strongly affected by errors in the time series models used to represent the autocorrelation. We also provide some analytical results comparing the robustness of the various methods with respect to time series modeling errors.