Testability allocation method based on inverse tangent function

被引:1
|
作者
Yang P. [1 ]
Hu Y. [1 ]
Wu W. [1 ]
Qiu J. [1 ]
机构
[1] Science and Technology on Integrated Logistics Support Laboratory, College of Intelligence Science and Technology, National University of Defense Technology, Changsha
关键词
Allocation function; Allocation weight; Inverse tangent function; Testability allocation; Testability index;
D O I
10.11887/j.cn.201906013
中图分类号
学科分类号
摘要
The existing testability allocation methods exist some unreasonable problems, such as the allocation index is too low or too high. The reason is that those methods apply linear allocation function which is not consistent with testability index. Therefore, a novel allocation function based on inverse tangent function was constructed, and the allocation algorithm of fault detection rate and fault isolation rate was presented. The comparative analysis among the proposed method and the classic fault rate allocation and comprehensive weighted allocation methods shows the superiority of the method proposed. With the increase of the allocation weight (fault rate), the amplification of allocation index gradually decreases, and there will be no unreasonable allocation index which is either too low or higher than 1. © 2019, NUDT Press. All right reserved.
引用
收藏
页码:83 / 87
页数:4
相关论文
共 11 条
  • [1] Qiu J., Liu G., Yang P., Et al., Equipment Testability Modeling and Design, pp. 110-135, (2013)
  • [2] Shen Q., Research on system testability allocation for equipment, (2004)
  • [3] Li J., Tao F., Jia C., Et al., Study on testability allocation method based on analytic hierarchy process, China Measurement & Test, 36, 2, pp. 30-33, (2010)
  • [4] Yang J.E., Hwang M.J., Sung T.Y., Et al., Application of genetic algorithm for reliability allocation in nuclear power plants, Reliability Engineering & System Safety, 65, 3, pp. 229-238, (1999)
  • [5] Elegbede C., Adjallah K., Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation, Reliability Engineering and System Safety, 82, 3, pp. 319-330, (2003)
  • [6] Harmanani H.M., Saliba R., An evolutionary algorithm for the testable allocation problem in high-level synthesis, Journal of Circuits, Systems, and Computers, 14, 2, pp. 347-366, (2005)
  • [7] Wang B., Huang K., Su L., Et al., Testability optimizing allocation of complicated electronic equipment based on genetic algorithms, Computer Measurement & Control, 15, 7, pp. 925-928, (2007)
  • [8] Zhang Y., Huang K., Chen J., Testability optimization allocation method based on genetic algorithm, Journal of Test and Measurement Technology, 25, 2, pp. 153-157, (2011)
  • [9] Zhang Q., Zhu C., Ran H., Et al., Testability index distribution method based on NSGA-Ⅱ algorithm, Journal of Nanjing University of Science and Technology, 36, 4, pp. 650-655, (2012)
  • [10] Liu G., Li F., Hu B., Research on testability optimization allocation method based on improved genetic algorithm, Fire Control & Command Control, 39, 1, pp. 44-47, (2014)