Agent technology to detect failures in continuous processes

Authors

DOI:

https://doi.org/10.56294/dm2024.423

Keywords:

agents, discrete-event systems, failure detection, reasoning schemes

Abstract

Introduction: To control a continuous production system whose components are exposed to failures, it is necessary to provide intelligence to the monitoring mechanism, due to the need to identify the type of failure, its source, and anticipate the consequences that arise from its occurrence. Methodology: In this paper, it is proposed to extend Sanz's multi-resolution model and apply it to a supervision scheme with event detection based on fuzzy logic and implemented using agent technology. Results: A mechanism to validate the implementation using discrete event simulation is also presented. Conclusions: It was concluded that discrete event simulation constitutes an appropriate way to validate a supervisory control system at a high level.

References

1. Lu Y, Xu X, Wang L. Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems. 2020;56:312–25. https://doi.org/10.1016/j.jmsy.2020.06.010

2. Dowdeswell B, Sinha R, MacDonell SG. Finding faults: A scoping study of fault diagnostics for Industrial Cyber–Physical Systems. Journal of Systems and Software. 2020;168:110638. https://doi.org/10.1016/j.jss.2020.110638

3. Bafandegan Emroozi V, Kazemi M, Doostparast M, Pooya A. Improving Industrial Maintenance Efficiency: a Holistic Approach to Integrated Production and Maintenance Planning with Human Error Optimization. Process Integration and Optimization for Sustainability. 2024;8(2):539–64. https://doi.org/10.1007/s41660-023-00374-3

4. Sanz R. Arquitectura de control inteligente de procesos [Tesis de doctorado]. Madrid: Universidad Politécnica de Madrid; 1990. http://www.aslab.org/documents/PhD/PhD-RSanz.pdf

5. Cerrada M, Aguilar J, Cardillo J, Faneite R. Agent-based design for fault management system in industrial processes. Revista Técnica de la Facultad de Ingeniería Universidad del Zulia. 2006;29:258–68. https://ve.scielo.org/scielo.php?script=sci_arttext&pid=S0254-07702006000300006

6. Guillén ML, Paredes JL, Camacho O. A proposal method for fault detection and diagnosis in chemical processes instrumentation using wavelet transform. Revista Técnica de la Facultad de Ingeniería Universidad del Zulia. 2005;28(1):68–77. https://ve.scielo.org/scielo.php?pid=S0254-07702005000100007&script=sci_abstract&tlng=en

7. Altamiranda EJ, Colina EM, Chacón ER. Intelligent supervisory systems for industrial process control. WSEAS Transactions on Systems. 2005;4(7):945–54. https://experts.illinois.edu/en/publications/intelligent-supervisory-systems-for-industrial-process-control

8. Parra Ortega C, Colina Morles E, Chacòn Ramírez E. Design framework for intelligent supervision of industrial processes. WSEAS Transactions on Systems. 2008;7(7):616–25. http://www.wseas.us/e-library/transactions/systems/2008/27-560.pdf

9. Barboza M, de Sousa Sobrinho JR, Dias J, dos Santos Filho DJ. Supervisory and Intelligent Systems. En: Bioengineering and Biomaterials in Ventricular Assist Devices. CRC Press; 2021. p. 111–32. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003138358-6/supervisory-intelligent-systems-marcelo-barboza-jos%C3%A9-ricardo-de-sousa-sobrinho-jonatas-dias-diolino-jos%C3%A9-dos-santos-filho

10. Kim SH, Song KR, Kang IY, Hyon CI. On-line set-point optimization for intelligent supervisory control and improvement of Q-learning convergence. Control Engineering Practice. 2021;114:104859. https://doi.org/10.1016/j.conengprac.2021.104859

11. Jiang Y, Yin S, Li K, Luo H, Kaynak O. Industrial applications of digital twins. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2021;379(2207):20200360. https://doi.org/10.1098/rsta.2020.0360

12. Nian R, Liu J, Huang B. A review On reinforcement learning: Introduction and applications in industrial process control. Computers & Chemical Engineering. 2020;139:106886. https://doi.org/10.1016/j.compchemeng.2020.106886

13. Achouch M, Dimitrova M, Ziane K, Sattarpanah Karganroudi S, Dhouib R, Ibrahim H, et al. On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences. 2022;12(16):8081. https://doi.org/10.3390/app12168081

14. Cassandras CG, Lafortune S. Introduction to Discrete Event Systems. Boston, MA: Springer US; 2008. http://link.springer.com/10.1007/978-0-387-68612-7

15. Llano LE, Moreno J. Análisis en línea de eventos de falla en sistemas de transmisión de electricidad usando SOE, lógica difusa y sistemas expertos. Revista Técnica de la Facultad de Ingeniería Universidad del Zulia. 2013;36(2):174–82. https://ve.scielo.org/scielo.php?pid=S0254-07702013000200009&script=sci_abstract&tlng=pt

16. Malagón Sáenz E, García Mogollón AM, García Mogollón JM. Estándares de Sostenibilidad de la Gestión del Talento Humano en el Marco del Global Reporting Iniciative (GRI), Algunos Indicadores en la Institución de Educación Superior. Universidad Santo Tomás Seccional Tunja Colombia. Prospectiva (1692-8261). 2024;22(1). https://openurl.ebsco.com/EPDB%3Agcd%3A4%3A10720996/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A178110907&crl=c

17. Rodríguez Fajardo LM, Donoso Anes A. Propuesta teórica de una metodología para el análisis de los riesgos empresariales por procesos y lógica difusa en el sector turístico cubano. Contaduría y Administración. 2022;67(3):348. https://doi.org/10.22201/fca.24488410e.2022.3474

18. Russell SJ, Norvig P. Artificial intelligence: a modern approach. Pearson; 2016. https://thuvienso.hoasen.edu.vn/handle/123456789/8967

19. Kowalski R. Computational logic and human thinking: how to be artificially intelligent. Cambridge University Press; 2011. https://books.google.es/books?hl=es&lr=&id=lRvWWjs8vu8C&oi=fnd&pg=PR9&ots=Nc2Vg_UziQ&sig=Lfe8g3T8LnNbgyQxp4uVeZOZAi0#v=onepage&q&f=false

20. Hernández Cruz AP, Parra Ortega CA, Portilla Granados LA. DISEÑO DE UNA ONTOLOGÍA PARA AGENTES QUE MONITOREAN MEDICIONES DE SENSORES. REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA). 2017;2(26). https://doi.org/10.24054/16927257.v26.n26.2015.2385

Downloads

Published

2024-09-05

Issue

Section

Original

How to Cite

1.
Parra Ortega CA, García Mogollón JM, Ramón Valencia JD. Agent technology to detect failures in continuous processes. Data and Metadata [Internet]. 2024 Sep. 5 [cited 2024 Oct. 13];3:.423. Available from: https://dm.ageditor.ar/index.php/dm/article/view/423