Advertisement

Multiple Fault Diagnosis in Manufacturing Processes and Machines Using Probabilistic Boolean Networks

  • Pedro J. Rivera TorresEmail author
  • Antônio José Silva Neto
  • Orestes Llanes Santiago
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

Developing methodologies for fault diagnosis in industrial/manufacturing systems is an active area of research. In this paper, a fault diagnosis scheme based on the Probabilistic Boolean Networks (PBN) model is proposed for a group of machines in a manufacturing process. The proposal takes into account the failure modes which affect the function and performance of the system. Firstly, the modes are identified and divided into two groups: faults and failures. The former implies detectable degradation of system function until the threshold for fault, which is eventual catastrophic loss of system, is surpassed. The latter leads to catastrophic fault. Then, using PBN, both classifications can be diagnosed and actions to mitigate them can be taken. The proposal also allows to forecast a time in hours by which the fault or failure will be imminent. The method herein discussed was applied to a ultrasound welding cycle, and a PBN model was created, simulated and verified through by means of model checking in PRISM. Results obtained show the validity of this methodology.

Keywords

Fault Detection and Isolation Probabilistic Boolean Networks Multiple faults Reliability 

Notes

Acknowledgements

The authors acknowledge the financial support provided by FAPERJ, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico; CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, research supporting agencies from Brazil; UPR-RP, University of Puerto Rico-Río Piedras; UERJ, Universidade do Estado do Rio de Janeiro and CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría.

References

  1. 1.
    Bachschmid, N., Pennacchi, P., Vania, A.: Identification of multiple faults in rotor systems. J. Sound Vib. 254, 327–366 (2002)CrossRefGoogle Scholar
  2. 2.
    Camps Echevarría, L., Campos Velho, H.F., Becceneri, J.C., Silva Neto, A.J., Llanes- Santiago, O.: The fault diagnosis inverse problem with ant colony optimization and ant colony optimization with dispersion. Appl. Math. Comput. 227(15), 687–700 (2014)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Camps Echevarría, L., Llanes-Santiago, O., Campos Velho, H.F., Silva Neto, A.J.: Fault Diagnosis Inverse Problems: Solution with Metaheuristics. Springer, Heidelberg (2019)CrossRefGoogle Scholar
  4. 4.
    Isermann, R.: Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems, vol. 24. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  5. 5.
    Kauffman, S.A.: Homeostasis and differentiation in random genetic control networks. Nature 224, 177–178 (1969)CrossRefGoogle Scholar
  6. 6.
    Kwiatkowska, M.Z., Norman, G., Parker, D.: Prism 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) Computer Aided Verification. Lecture Notes in Computer Science, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Mendoça, L., Sousa, J., da Costa, J.S.: An architecture for fault detection and isolation based on fuzzy methods. Expert Syst. Appl. 36, 1092–1104 (2009)CrossRefGoogle Scholar
  8. 8.
    Rivera Torres, P., Serrano Mercado, E.: Probabilistic Boolean network modeling as an aid for DFMEA in manufacturing systems. In: Proceedings of 18th Scientific Convention in Engineering and Architecture (CCIA 2016), La Habana, Cuba (2016)Google Scholar
  9. 9.
    Rivera Torres, P., Serrano Mercado, E., Llanes-Santiago, O., Anido Rifón, L.: Modeling preventive maintenance of manufacturing processes with Probabilistic Boolean Networks with Interventions. J. Intell. Manuf. (2015)Google Scholar
  10. 10.
    Rivera Torres, P.J., Serrano Mercado, E., Anido, R.L.: Probabilistic Boolean Network modeling of an industrial machine. J. Intell. Manuf. 29, 875–890 (2015)CrossRefGoogle Scholar
  11. 11.
    Rivera Torres, P.J., Serrano Mercado, E., Anido Rifón, L.: Probabilistic Boolean Network modeling and model checking as an approach for DFMEA for manufacturing systems. J. Intell. Manuf. 29, 1393–1413 (2015)CrossRefGoogle Scholar
  12. 12.
    Rodríguez Ramos, A., Domínguez Acosta, C., Rivera Torres, P.J., Serrano Mercado, E.I., Beauchamp Báez, G., Anido Rifón, L., Llanes-Santiago, O.: An approach to multiple fault diagnosis using fuzzy logic. J. Intell. Manuf. (2016)Google Scholar
  13. 13.
    Ruan, S., Zhou, Y., Feili, Y., Pattipati, K., Willett, P., Patterson-Hine, A.: Dynamic multiple-fault diagnosis with imperfect tests. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 39, 1224–1236 (2009)CrossRefGoogle Scholar
  14. 14.
    Shmulevich, I., Dougherty, E., Kim, S.: Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2), 261–274 (2002)CrossRefGoogle Scholar
  15. 15.
    Sobhani-Tehrani, E., Talebi, H., Khorasani, K.: Hybrid fault diagnosis of nonlinear systems using neural parameter estimators. Neural 50, 12–32 (2014)zbMATHGoogle Scholar
  16. 16.
    Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis-Part I: quantitative model-based methods. Comput. Chem. Eng. 27(3), 293–311 (2003)CrossRefGoogle Scholar
  17. 17.
    Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis-Part III: process history based methods. Comput. Chem. Eng. 27(3), 327–346 (2003)CrossRefGoogle Scholar
  18. 18.
    Wang, Z., Marek-Sadowska, M., Tsai, K., Rajski, J.: Analysis and methodology for multiple-fault diagnosis. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. 25, 558–575 (2006)CrossRefGoogle Scholar
  19. 19.
    Witczak, M.: Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems From Analytical to Soft Computing Approaches, vol. 354. Springer, Heidelberg (2007)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science - School of Natural SciencesUniversity of Puerto Rico-Río PiedrasSan JuanPuerto Rico
  2. 2.Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ)Nova FriburgoBrazil
  3. 3.Dpto. de Automática y ComputaciónUniversidad Tecnológica de la Habana José Antonio Echeverría (CUJAE)MarianaoCuba

Personalised recommendations