DOI QR코드

DOI QR Code

Percentile-based design of exponentially weighted moving average charts

지수가중이동평균 관리도의 백분위수 기반 설계

  • Jiyun Ku (Department of Applied Statistics, Chung-Ang University) ;
  • Jaeheon Lee (Department of Applied Statistics, Chung-Ang University)
  • 구지윤 (중앙대학교 응용통계학과) ;
  • 이재헌 (중앙대학교 응용통계학과)
  • Received : 2023.11.23
  • Accepted : 2024.01.14
  • Published : 2024.04.30

Abstract

The run length is defined as the number of samples or subgroups taken before the control chart statistic exceeds the control limits. Because the distribution of run length is typically asymmetric and has a large variability, it may not be appropriate to use ARL (average run length) alone to design control charts and evaluate performance. In this paper, we introduce the concept of percentile (PL)-based design of control charts, and propose the procedure for PL-based design of EWMA (exponentially weighted moving average) charts. For the PL-based design of EWMA, we present a fitted function for the control chart coefficient, given specific percentile parameters. Additionally, we perform simulations to compare the proposed design with the ARL-based design. The simulation results show that the proposed design yields improvements in monitoring in-control processes while maintaining the ability to detect out-of-control performance.

관리도에서 런길이는 관리 통계량이 관리한계를 벗어날 때까지 관측한 표본의 수로 정의한다. 일반적으로 런길이의 분포는 비대칭이 심하고 변동성이 크기 때문에 평균 런길이만 사용하여 관리도를 설계하고 성능을 평가하는 것은 적절하지 않을 수도 있다. 평균 런길이 기반 설계에 대한 대안으로 이 논문에서는 백분위수를 기반으로 한 관리도의 설계를 소개하고, 이를 지수가중이동평균 관리도의 설계에 적용하는 절차를 제안하고 있다. 이 절차는 백분위수 모수들이 주어진 경우, 모의실험을 통하여 적합된 함수를 사용하여 관리한계의 계수를 설정하는 것이다. 또한 모의실험을 수행하여 제안된 설계 절차를 평균 런길이 기반 설계와 비교하고 평가하였다. 모의실험 결과, 제안된 절차는 이상상태에서 탐지 능력은 거의 유지하면서 관리상태에서의 성능을 향상시킨다는 사실을 확인할 수 있었다.

Keywords

References

  1. Chakraborti S (2007). Run length distribution and percentiles: The Shewhart X chart with unknown parameters, Quality Engineering, 19, 119-127. https://doi.org/10.1080/08982110701276653
  2. Das N (2009). A comparison study of three non-parametric control charts to detect shift in location parameters, The International Journal of Advanced Manufacturing Technology, 41, 799-807. https://doi.org/10.1007/s00170-008-1524-3
  3. Faraz A, Saniga E, and Montgomery D (2018). Percentile-based control chart design with an application to Shewhart X and S2 control charts, Quality and Reliability Engineering International, 34, 1-11. https://doi.org/10.1002/qre.2384
  4. Gan FF (1993). An optimal design of EWMA control charts based on median run length, Journal of Statistical Computation and Simulation, 45, 169-184. https://doi.org/10.1080/00949659308811479
  5. Gan FF (1994). An optimal design of cumulative sum control chart based on median run length, Communications in Statistics-Simulation and Computation, 23, 485-503. https://doi.org/10.1080/03610919408813183
  6. Golosnoy V and Schmid W (2007). EWMA control charts for monitoring optimal portfolio weights, Sequential Analysis, 26, 195-224. https://doi.org/10.1080/07474940701247099
  7. Khoo MBC, Wong VH, Wu Z, and Castagliola P (2011). Optimal designs of the multivariate synthetic chart for monitoring the process mean vector based on median run length, Quality and Reliability Engineering International, 27, 979-1234. https://doi.org/10.1002/qre.1189
  8. Khoo MBC, Wong VH, Wu Z, and Castagliola P (2012). Optimal design of the synthetic chart for the process mean based on median run length, IIE Transactions, 44, 765-779. https://doi.org/10.1080/0740817X.2011.609526
  9. Lucas JM and Saccucci MS (1990). Exponentially weighted moving average control schemes: Properties and enhancements, Technometrics, 32, 1-12. https://doi.org/10.1080/00401706.1990.10484583
  10. Montgomery DC (2020). Introduction to Statistical Quality Control (8th ed), John Wiley & Sons, Hoboken, NJ.
  11. Roberts SW (1959). Control chart tests based on geometric moving averages, Technometrics, 1, 239-250. https://doi.org/10.1080/00401706.1959.10489860
  12. Ryan TP (2000). Statistical Methods for Quality Improvement (2nd ed), John Wiley & Sons, New York, NY.
  13. Woodall WH (1985). The statistical design of quality control charts, Statistician, 34, 155-160. https://doi.org/10.2307/2988154