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

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14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) (SOCO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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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.

Author information

Authors and Affiliations

  1. Department of Computer Science - School of Natural Sciences, University of Puerto Rico-Río Piedras, 17 Ave Universidad STE 1701, San Juan, PR, 00925-2537, Puerto Rico

    Pedro J. Rivera Torres

  2. Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Rua Bonfim 25 - Campus UERJ, Vila Amélia, Nova Friburgo, RJ, 28625-570, Brazil

    Antônio José Silva Neto

  3. Dpto. de Automática y Computación, Universidad Tecnológica de la Habana José Antonio Echeverría (CUJAE), Calle 114, no. 11901, 19390, Marianao, La Habana, Cuba

    Orestes Llanes Santiago

Authors
  1. Pedro J. Rivera Torres
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  2. Antônio José Silva Neto
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  3. Orestes Llanes Santiago
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Corresponding author

Correspondence to Pedro J. Rivera Torres .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Rivera Torres, P.J., Neto, A.J.S., Llanes Santiago, O. (2020). Multiple Fault Diagnosis in Manufacturing Processes and Machines Using Probabilistic Boolean Networks. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_34

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