Program Information
Application of Moving Centerline Exponentially Weighted Moving Average (MCEWMA) Control Chart to Detect Real Warning for Daily Range QA in Proton Beams
J Lah1*, D Shin2 , G Kim3 , (1) MyongJi Hospital, Goyang-si, Gyeonggi-do, (2) National Cancer Ceneter, Goyang-si, Gyeonggi-do, (3) University of California, San Diego, La Jolla, CA
Presentations
SU-I-GPD-T-240 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: To verify the usefulness of moving centerline exponentially weighted moving average (MCEWMA) chart, as compared an autoregressive integrated moving average (ARIMA) two-stage time series model, and MCEWMA approach, specific to detect the small variation detection in the daily range QA process.
Methods: A total of 150 individual observations were taken on each of daily range measurements over a period of time in defined daily interval. The conventional control chart recommended for individual observation is primarily based on the assumption that the process data are independent and normally distributed. If the data set is correlated, conventional control charts can cause a substantial increase in the false alarm rate. For this reason, QA observations were analyzed using the autocorrelation function to find appropriate time series models. An ARIMA model and an MCEWMA control chart were both applied to identical range data.
Results: An autocorrelation function plot for daily range data at lag 1 and 2 result in r1=0.63 and r2=0.4, respectively. This is certainly large enough to severely distort control chart performance. In the ARIMA and MCEWMA charts, 3 points fell out of the limits. This was the same result as the two-stage time series modeling and the special control chart, MCEWMA. Both indicate the range QA process is reasonably stable, with out-of-control signals where an assignable cause may be present. In the ARIMA model, applying control charts to several process variables and developing an explicit time series model for each variable of interest is potentially time-consuming. However, MCEWMA chart is simple to implement with comparable results.
Conclusion: The MCEWMA charts can detect meaningful, small shifts for individual data responses that are autocorrelated. This case study confirms the suitability of MCEWMA which is recommended as an alternative to the time series model and residual special control chart approach.
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