Advertisement

Sequential fault diagnosis using an inertial velocity differential evolution algorithm

  • Xiao-Hong QiuEmail author
  • Yu-Ting Hu
  • Bo Li
Research Article

Abstract

The optimal test sequence design for fault diagnosis is a challenging NP-complete problem. An improved differential evolution (DE) algorithm with additional inertial velocity term called inertial velocity differential evolution (IVDE) is proposed to solve the optimal test sequence problem (OTP) in complicated electronic system. The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm. And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate (FIR) satisfied and generate diagnostic decision tree to decrease the test sets and the test cost. The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR. Compared with the other algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA), IVDE can get better solution to OTP.

Keywords

Differential evolution (DE) evolutionary computation fault isolation rate (FIR) testability fault diagnosis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    K. R. Pattipati, M. Alexandridis. Application of heuristic search and information theory to sequential fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, no. 4, pp. 872–887, 1990.zbMATHCrossRefGoogle Scholar
  2. [2]
    V. Raghavan, M. Shakeri, K. R. Pattipati. Optimal and near-optimal test sequencing algorithms with realistic test models. IEEE Transactions on Systems, Man, and Cybernetics, vol. 29, no. 1, pp. 11–27, 1999.CrossRefGoogle Scholar
  3. [3]
    M. Shakeri, V. Raghavan, K. R. Pattipati, A. Patterson-Hine. Sequential testing algorithms for multiple fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, vol. 30, no. 1, pp. 1–14, 2000.CrossRefGoogle Scholar
  4. [4]
    Z. Hu, S. G. Zhang, Y. M. Yang, L. J. Song, Y. Liu. Test sequencing problem considering life cycle cost based on the tests with non-independent cost. Chemical Engineering Transactions, vol. 33, no. 3, pp. 253–258, 2013.Google Scholar
  5. [5]
    F. Tu, K. R. Pattipati, S. Deb, V. N. Malepati. Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems. IEEE Transactions on Systems, Man, and Cybernetics, vol. 33, no. 1, pp. 73–85, 2003.CrossRefGoogle Scholar
  6. [6]
    O. E. Kundakcioglu, T. Ünlüyurt. Bottom-up construction of minimum-cost and/or trees for sequential fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics, vol. 37, no. 5, pp. 621–629, 2007.CrossRefGoogle Scholar
  7. [7]
    C. L. Dong, Q. Zhang, S. C. Geng. A modeling and probabilistic reasoning method of dynamic uncertain causality graph for industrial fault diagnosis. International Journal of Automation and Computing, vol. 11, no. 3, pp. 288–298, 2014.CrossRefGoogle Scholar
  8. [8]
    J. S. Yu, B. Xu, X. S. Li. Generation of test strategy for sequential fault diagnosis based on genetic algorithms. Journal of System Simulation, vol. 16, no. 4, pp. 833–836, 2004. (in Chinese)Google Scholar
  9. [9]
    R. H. Jiang, H. J. Wang, B. Long. Applying improved AO* based on DPSO algorithm in the optimal test sequencing problem of large scale complicated electronic system. Chinese Journal of Computers, vol. 31, no. 10, pp. 1835–1840, 2008. (in Chinese)CrossRefGoogle Scholar
  10. [10]
    G. Y. Lian, K. L. Huang, J. H. Chen, F. Q. Gao. Optimization method for diagnostic sequence based on improved particle swarm optimization algorithm. Journal of Systems Engineering and Electronics, vol. 20, no. 4, pp. 899–995, 2009. (in Chinese)Google Scholar
  11. [11]
    X. H. Qiu, J. Liu, X. H. Qiu. Random DBPSO algorithm application in the optimal test-sequencing problem of complicated electronic system. In Proceedings of the 2nd International Conference on Computer and Automation Engineering, IEEE, Singapore, vol. 1, pp. 107–111, 2010.Google Scholar
  12. [12]
    P. R. Srivatsava, B. Mallikarjun, X. S. Yang. Optimal test sequence generation using firefly algorithm. Swarm and Evolutionary Computation, vol. 8, pp. 44–53, 2013.CrossRefGoogle Scholar
  13. [13]
    J. L. Pan, X. H. Ye, Q. Xue. A new method for sequential fault diagnosis based on Ant Algorithm. In Proceedings of the 2nd International Symposium on Computational Intelligence and Design, IEEE, Changsha, China, vol. 1, pp. 44–48, 2009.Google Scholar
  14. [14]
    Z. L. Pan, L. Chen, G. Z. Zhang. Cultural algorithm for minimization of binary decision diagram and its application in crosstalk fault detection. International Journal of Automation and Computing, vol. 7, no. 1, pp. 70–77, 2010.CrossRefGoogle Scholar
  15. [15]
    C. L. Yang, J. H. Yan, B. Long, Z. Liu. A novel test optimizing algorithm for sequential fault diagnosis. Microelectronics Journal, vol. 45, no. 6, pp. 719–727, 2014.CrossRefGoogle Scholar
  16. [16]
    J. Zhang, A. C. Sanderson. JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945–958, 2009.CrossRefGoogle Scholar
  17. [17]
    Y. Wang, Z. X. Cai, Q. F. Zhang. Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011.MathSciNetCrossRefGoogle Scholar
  18. [18]
    J. Kennedy, R. C. Eberhart. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, IEEE, Piscataway, USA, vol. 4, pp. 1942–1948, 1995.CrossRefGoogle Scholar
  19. [19]
    J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006.CrossRefGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Software SchoolJiangxi University of Science and TechnologyNanchangChina
  2. 2.College of Information EngineeringJiangxi University of Science and TechnologyGanzhouChina

Personalised recommendations