Applications of the Fault Diagnosis: Inverse Problem Methodology to Benchmark Problems

  • Lídice Camps Echevarría
  • Orestes Llanes Santiago
  • Haroldo Fraga de Campos Velho
  • Antônio José da Silva Neto
Part of the Studies in Computational Intelligence book series (SCI, volume 763)


This chapter presents the application of the last three steps (2, 3, and 4) of the Fault Diagnosis—Inverse Problem Methodology (FD-IPM), as described in Sect.  2.1, to the three benchmark problems that were presented in Sect.  2.4. Step 2 is to evaluate the faults for which only the detection is possible. The third step deals with the solution of an optimization problem with metaheuristics. The fourth step provides the conclusion on the diagnosis of the system, based on the results from the third step. The experiments with the DC Motor, Inverted Pendulum System and Two Tanks System are presented in Sects. 4.2, 4.3 and 4.4, respectively. The Inverted Pendulum System is affected by faults which cannot be diagnosed, only detected. For that reason, Step 2 was only applied to the The Inverted Pendulum System.


  1. 23.
    Carlisle, A., Dozier, G.: An off-the-self PSO. In: Proceedings of the Particle Swarm Optimization Workshop, Indiana, pp. 1–6 (2001)Google Scholar
  2. 25.
    Chow, E.Y., Willsky, A.: Analytical redundancy and the design of robust failure detection systems. IEEE Trans. Autom. Control 29, 603–614 (1984)MathSciNetCrossRefGoogle Scholar
  3. 30.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical test as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)CrossRefGoogle Scholar
  4. 31.
    Ding, S.X.: Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer, Berlin (2008)Google Scholar
  5. 42.
    Frank, P.M.: Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy – a survey and some new results. Automatica 26(3), 459–474 (1990)MathSciNetCrossRefGoogle Scholar
  6. 45.
    García, S., Molina, D., Lozano, M., Herrera, F.: 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 (2009)CrossRefGoogle Scholar
  7. 54.
    Hoefling, T.: Detection of parameter variations by continuous-time parity equations. In: 12th IFACWorld-Congress, pp. 511–516 (1993)Google Scholar
  8. 55.
    Hoefling, T., Isermann, R.: Fault detection based on adaptive parity equations and single-parameter tracking. Control Eng. Pract. 4(10), 1361–1369 (1996)CrossRefGoogle Scholar
  9. 65.
    Kameyama, K.: Particle swarm optimization – a survey. IEICE Trans. Inf. Syst. E92-D(7), 1354–1361 (2009)CrossRefGoogle Scholar
  10. 70.
    Kennedy, J.: Chapter The behavior of particles. In: Evolutionary Programming VII: Proceeding of the Seventh Annual Conference on Evolutionary Programming (EP98). Lecture Notes in Computer Science, vol. 1447, pp. 581–590. Springer, New York (1998)Google Scholar
  11. 111.
    Silva Neto, A.J., Becceneri, J.C., Campos Velho, H.F. (eds.): Computational Intelligence Applied to Inverse Problems in Radiative Transfer. EdUERJ, Rio de Janeiro, Brazil (2016)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Lídice Camps Echevarría
    • 1
  • Orestes Llanes Santiago
    • 2
  • Haroldo Fraga de Campos Velho
    • 3
  • Antônio José da Silva Neto
    • 4
  1. 1.Centro de Estudios de MatemáticaUniversidad Tecnológica de La Habana José, Antonio Echeverría, CUJAEMarianaoCuba
  2. 2.Dpto. de Automática y ComputaciónUniversidad Tecnológica de La Habana José, Antonio Echeverría, CUJAEMarianaoCuba
  3. 3.National Institute for Space Research, INPESão José dos CamposBrazil
  4. 4.Instituto PolitécnicoUniversidade do Estado do Rio de Janeiro, UERJNova FriburgoBrazil

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