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Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions

Author

Listed:
  • Pedro J. Rivera Torres

    (ETSET-Universidade de Vigo)

  • Eileen I. Serrano Mercado

    (Polytechnic University of Puerto Rico)

  • Orestes Llanes Santiago

    (Instituto Superior Politécnico José A. Echevarría (CUJAE))

  • Luis Anido Rifón

    (ETSET-Universidade de Vigo)

Abstract

Recent developments in intelligent manufacturing have validated the use of probabilistic Boolean networks (PBN) to model failures in manufacturing processes and as part of a methodology for Design Failure Mode and Effects Analysis (DFMEA). This paper expands the application of PBNs in manufacturing processes by proposing the use of interventions in PBNs to model an ultrasound welding process in a preventive maintenance (PM) schedule, guiding the process to avoid failure and extend its useful work life. This bio-inspired, stochastic methodology uses PBNs with interventions to model manufacturing processes under a PM schedule and guides the evolution of the network, providing a new mechanism for the study and prediction of the future behavior of the system at the design phase, assessing future performance, and identifying areas to improve design reliability and system resilience. A process engineer designing manufacturing processes may use this methodology to create new or improve existing manufacturing processes, assessing risk associated with them, and providing insight into the possible states, operating modes, and failure modes that can occur. The engineer can also guide the process and avoid states that can result in failure, and design an appropriate PM schedule. The proposed method is applied to an ultrasound welding process. A PBN with interventions model was simulated and verified using model checking in PRISM, generating data required to conduct inferential statistical tests to compare the effects of probability of failures between the PBN and PBN with Interventions models. The obtained results demonstrate the validity of the proposed methodology.

Suggested Citation

  • Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1941-1952, December.
  • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1226-x
    DOI: 10.1007/s10845-016-1226-x
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    References listed on IDEAS

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    Cited by:

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    2. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    3. Chayma Sellami & Carlos Miranda & Ahmed Samet & Mohamed Anis Bach Tobji & François de Beuvron, 2020. "On mining frequent chronicles for machine failure prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1019-1035, April.

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