TY - GEN
T1 - A partitioning method of experimental levels for low failure probability estimation problems
AU - Song, Kunling
AU - Zhang, Yugang
AU - Yu, Xinshui
AU - Song, Bifeng
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - Failure boundaries are always far away from the center of the design space of variables for low failure probability problems. In order to make full use of samples, a partitioning method of experimental levels is proposed for improving general design of experiments in this paper. The method is implemented by non-uniformly partitioning experimental levels according to the probability density function of variables, which leads to a wide interval of adjacent experimental levels at the high probability density value, while a narrow one at the low. The translational propagation latin hypercube design was improved by using the non-uniformly partitioning experimental levels method. To validate the practicability and effectiveness of the proposed method, two numerical examples are presented and the results show that the improved translational propagation latin hypercube design is more effective than the previous.
AB - Failure boundaries are always far away from the center of the design space of variables for low failure probability problems. In order to make full use of samples, a partitioning method of experimental levels is proposed for improving general design of experiments in this paper. The method is implemented by non-uniformly partitioning experimental levels according to the probability density function of variables, which leads to a wide interval of adjacent experimental levels at the high probability density value, while a narrow one at the low. The translational propagation latin hypercube design was improved by using the non-uniformly partitioning experimental levels method. To validate the practicability and effectiveness of the proposed method, two numerical examples are presented and the results show that the improved translational propagation latin hypercube design is more effective than the previous.
KW - density function
KW - design of experiments
KW - experimental levels
KW - low failure probability
UR - http://www.scopus.com/inward/record.url?scp=85009910350&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2016.7798105
DO - 10.1109/IEEM.2016.7798105
M3 - 会议稿件
AN - SCOPUS:85009910350
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 1387
EP - 1391
BT - 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
PB - IEEE Computer Society
T2 - 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
Y2 - 4 December 2016 through 7 December 2016
ER -