Optimal Selection of Imperfect Tests Based on Improved Quantum-Inspired Evolutionary Algorithm

被引:0
|
作者
Lei H.-J. [1 ]
Qin K.-Y. [2 ]
机构
[1] School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, Sichuan
[2] School of Aeronautics & Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan
来源
| 1600年 / Chinese Institute of Electronics卷 / 45期
关键词
Design for testability; Imperfect tests; Quantum-inspired evolutionary algorithm; Test selection;
D O I
10.3969/j.issn.0372-2112.2017.10.022
中图分类号
学科分类号
摘要
Optimal selection of tests is an important problem that arises in design for testability for complex electronic systems. Firstly, from the perspective of test tolerance, the reason of how tests produce miss detection and false alarm is analyzed. Then, a new mathematic model for the problem of test selection in the presence of imperfect tests is developed. It consists of minimizing the sum of test cost, miss detection cost and false alarm cost, subject to lower bound constraints on fault detection rate and fault isolation rate. To optimize the model, an improved quantum-inspired evolutionary algorithm is proposed. It is formed by making some improvements to an extant algorithm that has been used for optimal selection of perfect tests, including population initialization, fitness calculation and the strategy of population evolution. Finally, two simulation examples are used to validate the effectiveness and superiority of the solution method and the model. © 2017, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码:2464 / 2472
页数:8
相关论文
共 23 条
  • [11] Ma L., Li H.-J., Wang C.-G., Et al., Optimized test selection method considering the cost of alternative maintenance, Chinese Journal of Scientific Instrument, 36, 2, pp. 280-286, (2015)
  • [12] Bhushan M., Rengaswamy R., Design of sensor location based on various fault diagnostic observability and reliability criteria, Computers and Chemical Engineering, 24, 2-7, pp. 735-741, (2000)
  • [13] Yang G., Liu G.-J., Li J.-G., Et al., Optimal sensor placement based on various fault detectability and reliability criteria, Acta Electronica Sinica, 34, 2, pp. 348-351, (2006)
  • [14] Zhu X.-H., Li Y.-H., Li N., Et al., Optimal sensor placement design based on improved discrete PSO algorithm, Acta Electronica Sinica, 41, 10, pp. 2104-2108, (2013)
  • [15] Ma L., Li H.-J., Wang C.-G., Et al., Optimal sensor placement based on improved discrete PSO algorithm, Acta Electronica Sinica, 43, 12, pp. 2408-2413, (2015)
  • [16] Zhang S.G., Pattipati K.R., Hu Z., Et al., Optimal selection of imperfect tests for fault detection and isolation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43, 6, pp. 1370-1384, (2013)
  • [17] Luo J.H., Tu H.Y., Pattipati K., Et al., Diagnosis knowledge representation and inference, IEEE Instrumentation and Measurement Magazine, 9, 4, pp. 45-52, (2006)
  • [18] Sheppard J.W., Butcher S.G.W., A formal analysis of fault diagnosis with D-matrices, Journal of Electronic Testing: Theory and Applications, 23, 4, pp. 309-322, (2007)
  • [19] Han K.H., Kim J.H., Quantum-inspired evolutionary algorithm for a class of combinatorial optimization, IEEE Transactions on Evolutionary Computation, 6, 6, pp. 580-593, (2002)
  • [20] Han K.H., Kim J.H., Quantum-inspired evolutionary algorithms with a new termination criterion, H<sub>ε</sub> gate, and two-phase scheme, IEEE Transactions on Evolutionary Computation, 8, 2, pp. 156-169, (2004)