TY - GEN
T1 - Interval estimation of process capability indices based on the weibull distributed quality data of supplier products
AU - Cui, Yanhe
AU - Yang, Jun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/5
Y1 - 2018/12/5
N2 - Process capability indices (PCIs) play an important role in analyzing process quality capability. However, the occurrence of data fraud events indicates that suppliers may provide false information, which may result in inappropriate choices for customers. Thus, to estimate PCIs and further check authenticity of data provided by suppliers, it is necessary to carry out process capability analysis from supplier products. The quality data of supplier products are doubly truncated based on technical requirements. Considering many quality characteristics of products from practical processes follow Weibull distributions, we propose an interval estimation method of PCIs using the truncated Weibull data. First, Monte Carlo-EM algorithm is applied to estimate unknown parameters. Then, a quantile-filling algorithm is adopted to transform Weibull truncated data into pseudo-complete data. After pseudo-complete data are obtained, we apply generalized confidence interval to calculate interval estimation of PCIs. Finally, an example is provided to illustrate the implement of the proposed method.
AB - Process capability indices (PCIs) play an important role in analyzing process quality capability. However, the occurrence of data fraud events indicates that suppliers may provide false information, which may result in inappropriate choices for customers. Thus, to estimate PCIs and further check authenticity of data provided by suppliers, it is necessary to carry out process capability analysis from supplier products. The quality data of supplier products are doubly truncated based on technical requirements. Considering many quality characteristics of products from practical processes follow Weibull distributions, we propose an interval estimation method of PCIs using the truncated Weibull data. First, Monte Carlo-EM algorithm is applied to estimate unknown parameters. Then, a quantile-filling algorithm is adopted to transform Weibull truncated data into pseudo-complete data. After pseudo-complete data are obtained, we apply generalized confidence interval to calculate interval estimation of PCIs. Finally, an example is provided to illustrate the implement of the proposed method.
KW - Monte Carlo-EM algorithm
KW - confidence interval
KW - process capability indices
KW - quantile-filling algorithm
KW - supplier products
UR - https://www.scopus.com/pages/publications/85060702908
U2 - 10.1109/DSA.2018.00024
DO - 10.1109/DSA.2018.00024
M3 - 会议稿件
AN - SCOPUS:85060702908
T3 - Proceedings - 2018 5th International Conference on Dependable Systems and Their Applications, DSA 2018
SP - 86
EP - 90
BT - Proceedings - 2018 5th International Conference on Dependable Systems and Their Applications, DSA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Dependable Systems and Their Applications, DSA 2018
Y2 - 22 September 2018 through 23 September 2018
ER -