• Title/Summary/Keyword: Double sampling chart

Search Result 2, Processing Time 0.016 seconds

Economic-Statistical Design of Double Sampling T2 Control Chart under Weibull Failure Model (와이블 고장모형 하에서의 이중샘플링 T2 관리도의 경제적-통계적 설계 (이중샘플링 T2 관리도의 경제적-통계적 설계))

  • Hong, Seong-Ok;Lee, Min-Koo;Lee, Jooho
    • Journal of Korean Society for Quality Management
    • /
    • v.43 no.4
    • /
    • pp.471-488
    • /
    • 2015
  • Purpose: Double sampling $T^2$ chart is a useful tool for detecting a relatively small shift in process mean when the process is controlled by multiple variables. This paper finds the optimal design of the double sampling $T^2$ chart in both economical and statistical sense under Weibull failure model. Methods: The expected cost function is mathematically derived using recursive equation approach. The optimal designs are found using a genetic algorithm for numerical examples and compared to those of single sampling $T^2$ chart. Sensitivity analysis is performed to see the parameter effects. Results: The proposed design outperforms the optimal design of the single sampling $T^2$ chart in terms of the expected cost per unit time and Type-I error rate for all the numerical examples considered. Conclusion: Double sampling $T^2$ chart can be designed to satisfy both economic and statistical requirements under Weibull failure model and the resulting design is better than the single sampling counterpart.

An Adaptive Synthetic Control Chart for Detecting Shifts in the Process Mean (공정평균 이동을 탐지하기 위한 적응 합성 관리도)

  • Lim Taejin
    • Journal of Korean Society for Quality Management
    • /
    • v.32 no.4
    • /
    • pp.169-183
    • /
    • 2004
  • The synthetic control chart (SCC) proposed by Wu and Spedding (2000) is to detect shifts in the process mean. The performance was re-evaluated by Davis and Woodall (2002), and the steady-state average run length (ARL) performance was shown to be inferior to cumulative sum (CUSUM) or exponentially weighted moving average (EWMA) chart This paper proposes a simple adaptive scheme to improve the performance of the synthetic control chart. That is, once a non-conforming (NC) sample occurs, we investigate the next L-consecutive samples with larger sample sizes and shorter sampling intervals. We employ a Markov chain model to derive the ARL and the average time to s19na1 (ATS). We also propose a statistical design procedure for determining decision variables. Comprehensive comparative study shows that the proposed control chart is uniformly superior to the original SCC or double sampling (DS) Χ chart and comparable to the EWMA chart in ATS performance.