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)


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.


Fault Detection and Isolation Probabilistic Boolean Networks Multiple faults Reliability 



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.


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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

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