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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.1 México Jan./Mar. 2014

http://dx.doi.org/10.13053/CyS-18-1-2014-015 

Artículos

 

An Approach to Fault Diagnosis Using Meta-Heuristics: a New Variant of the Differential Evolution Algorithm

 

Un enfoque al diagnóstico de fallos aplicando meta heurísticas: nueva variante del algoritmo Evolución Diferencial

 

Lídice Camps Echevarría1, Orestes Llanes Santiago1, Antônio J. Silva Neto2, and Haroldo Fraga de Campos Velho3

 

1 Instituto Superior Politécnico José Antonio Echeverría, CUJAE, Havana, Cuba. lidice@cemat.cujae.edu.cu, orestes@electrica.cujae.edu.cu

2 State University of Rio de Janeiro, IPRJ-UERJ, Rio de Janeiro, Brazil. ajsneto@iprj.uerj.br

3 National Institute of Spatial Researchs, INPE, São Jose dos Campos, SP, Brazil. haroldo@lac.inpe.br

 

Abstract

This paper presents an application of meta-heuristics to fault diagnosis. The idea behind this application is to develop methods for fault diagnosis that should be robust, sensitive and with an adequate computational cost. Applications of meta-heuristics are possible based on the formulation of fault diagnosis as an optimization problem. The results indicate the suitability of the use of meta-heuristics for fault diagnosis. In particular, this study shows an application of meta-heuristic termed Differential Evolution to diagnosing a DC Motor benchmark. This allowed developing a new variant of Differential Evolution, namely, Differential Evolution with Particle Collision. This new algorithm was validated with some benchmark functions for continuous optimization, showing that it over-performed the behavior of Differential Evolution.

Keywords: Differential evolution, meta-heuristics, fault diagnosis, particle collision, robustness, sensitivity.

 

Resumen

Este trabajo presenta un estudio de la aplicación de meta heurísticas al diagnóstico de fallos, con el fin de desarrollar métodos que sean robustos ante perturbaciones, sensibles ante fallos incipientes y con adecuado costo computacional. La aplicación de las mismas es posible a partir de la formulación del diagnóstico de fallos como un problema de optimización. Los resultados indican la factibilidad del uso de meta heurísticas. En este estudio se aplicó la meta heurística, Evolución diferencial al diagnóstico de fallos en el sistema de prueba Motor CD. El estudio permitió desarrollar un nuevo algoritmo que se ha llamado Evolución diferencial con colisión de partículas. Este fue validado con funciones de prueba de optimización continúa mostrando su superioridad sobre Evolución diferencial.

Palabras clave: Colisión de partículas, diagnóstico de fallos, evolución diferencial, meta heurísticas, robustez sensibilidad.

 

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References

1. Brest, J., Greiner, S., Boscovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(8), 646 - 657.         [ Links ]

2. Campos-Knupp, D., Silva-Neto, A., & Figueiredo-Sacco, W. (2007). Estimation of radiative properties with the particle collision algorithm. In Inverse Problems, Design and Optimization Symposium. Miami, Florida, USA.         [ Links ]

3. da Luz, E. P., Becceneri, J., & Velho, H. D. C. (2008). A new multiparticle collision algorithm for optimization in a high-performance environments. Journal of Computational InterdisciplinarySciences, 1(1), 3-10.         [ Links ]

4. Das, S., Abraham, A., Uday, K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation, 13(3), 526 -553.         [ Links ]

5. Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical test as a methodology for comparing evolutionary and swarm intellgence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.         [ Links ]

6. Ding, S. X. (2008). Model- based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer.         [ Links ]

7. Figuereido-Sacco, W., Oliveira, C., & Pereira, C. (2006). Two stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy, 48(6), 525 - 539.         [ Links ]

8. Frank, P. M. (1996). Analytical and qualitative model- based fault diagnosis- a survey and some new results. European Journal of Control, 26(3), 459-474.         [ Links ]

9. García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the CEC 2005 Special Session on Real Parameter Optimization. J. Heuristics, 15(6), 617- 644.         [ Links ]

10. Goldberg, D. E. (1989). Genetic Algorithms in search , optimization, and machine learning. Reading, MA: Addison-Wesley.         [ Links ]

11. Gong, W., Cai, Z., & Ling, C. X. (2010). DE / BBO a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing - A Fusion of Foundations, Methodologies and Applications Archive, 15(4), 645 -665.         [ Links ]

12. Isermann, R. (2005). Model based fault detection and diagnosis. Status and applications. Annual Reviews in Control, 29(1), 71-85.         [ Links ]

13. Liu, Q. & Wenyuan, L. (2009). The Study of Fault Diagnosis Based on Particle Swarm Optimization algorithm. Computer and Information Science, 2(2), 87 - 91.         [ Links ]

14. Lobato, F., Steffen, V., & Silva-Neto, A. (2009). Solution of inverse radiative transfer problems in two-layer participating media with differential evolution. Inverse Problems in Science and Engineering, 18(2), 183-195.         [ Links ]

15. Metenidin, M., Witczak, M., & Korbicz, J. (2011). A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem. Engineering Applications of Artificial Intelligence, 24, 958 - 967.         [ Links ]

16. Mezura-Montes, E., Velazquez-Reyes, J., & Coello-Coello, C. (2006). A comparative study of differential evolution variants for global optimization. In GECCO 06, Seattle, Washington, USA.         [ Links ]

17. Qin, A., Huang, V., & Suganthan, P. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398 - 417.         [ Links ]

18. Sacco, W. & Oliveira, C. (2005). A new stochastic optimization algorithm based on particle collisions. In 2005 ANS Annual Meeting, Transactions of the American Nuclear Society.         [ Links ]

19. Samanta, B. & Nataraj, C. (2009). Use of particle swarm optimization for machinery fault detection. Engineering Applications of Artificial Intelligence, 22(2), 308 - 316.         [ Links ]

20. Silva-Neto, A. & Becceneri, J. (2011). Inteligenca Computacional Aplicada a Probl mas Inv rsos m Transferencia Radiativa. SBMAC.         [ Links ]

21. Simani, S., Fantuzzi, C., & Patton, R. J. (2002). Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques. Springer-Verlag.         [ Links ]

22. Simani, S. & Patton, R. J. (2008). Fault diagnosis of an industrial gas turbine prototype using a system identification approach. Control Eng. Practice, 16(7), 769-786.         [ Links ]

23. Storn, R. & Price, K. (1997). Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341 - 359.         [ Links ]

24. Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the cec 2005 special session on real parameter optimization. Technical report, Nanyang Technological University.         [ Links ]

25. Tvrdik, J. (2009). Adaptation in differential evolution: A numerical comparison. Applied Soft Computing, 9(3), 1149 - 1155.         [ Links ]

26. Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers and Chemical Engineering, 27(3), 293-311.         [ Links ]

27. Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis Part II: Qualitative model-based methods and search strategies. Computers and Chemical Engineering, 27(3), 313-326.         [ Links ]

28. Wang, L., Niu, Q., & Fei, M. (2008). A novel quantum ant colony optimization algorithm and its application to fault diagnosis. Transactions of the Institute of Measurement and Control, 30(3/4), 313-329.         [ Links ]

29. Witczak, M. (2006). Advances in model based fault diagnosis with evolutionary algorithms and neural networks. Int. J. Appl. Math. Comput. Sci., 16(1), 85-99.         [ Links ]

30. Witczak, M. (2007). Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems From Analytical to Soft Computing Approaches. Springer.         [ Links ]

31. Yang, E., Xiang, H., Guand, D., & Zhang, Z. (2007). A comparative study of genetic algorithm parameters for the inverse problem-based fault diagnosis of liquid rocket propulsion systems. International Journal of Automation and Computing, 4(3), 255-261.         [ Links ]

32. Yang, K. T., Z. & Yao, X. (2008). Self-adaptive differential evolution with neighborhood search. In IEEE Congress on Evolutionary Computation (CEC2008). Hong Kong, 1110-1116.         [ Links ]

33. Zaharie, D. (2009). Influence of crossover on the behavior of differential evolution algorithms. Applied Soft Computing, 9(3), 1126-1138.         [ Links ]

34. Zhang, J. (2009). Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945-958.         [ Links ]

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