Ontology-driven generation of Bayesian diagnostic models for assembly systems


A major challenge limiting the practical adoption of Bayesian networks for diagnosis in manufacturing systems is the difficulty of constructing the models from expert knowledge. A key possibility for tackling this limitation is believed to be through utilising the available sources of design information that is readily available as part of the engineering design process. Some of the most notable sources of such design information include formal domain models such as product-process-equipment design ontologies which are becoming a widely accepted mean for formally capturing and communicating design information. This makes these ontologies a valuable knowledge source for automatic and semi-automatic generation of Bayesian networks, instead of the entirely expert-driven traditional approach. However, design ontologies lack on the fault-related information side as they are primarily aimed at capturing the intended behaviour of the designed system. To bridge this gap, we propose integrating failure mode and effect analysis (FMEA) information into design ontologies and using the resulting integral models for the generation of Bayesian diagnostic networks. We also propose a method for the generation process and demonstrate the validity of the approach with an industrial case study.

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  1. 1.

    Ribeiro L, Barata J (2011) Re-thinking diagnosis for future automation systems: an analysis of current diagnostic practices and their applicability in emerging IT based production paradigms. Comput Ind 62(7):639–659

  2. 2.

    Ding Z, Peng Y, Pan R (2006) BayesOWL: uncertainty modeling in semantic web ontologies. Soft Comput Ontol Semant Web 204:3–29

    Article  Google Scholar 

  3. 3.

    Laskey KB, Costa PC (2005) Of klingons and starships: Bayesian logic for the 23rd century. Relation 4:05–04

  4. 4.

    Hu SJ, Koren Y (1997) Stream-of-variation theory for automotive body assembly. CIRP Ann Manuf Technol 46(1):1–6

    Article  Google Scholar 

  5. 5.

    Bae YH, Lee SH, Kim HC, Lee BR, Jang J, Lee J (2006) A real-time intelligent multiple fault diagnostic system. Int J Adv Manuf Technol 29(5):590–597

    Article  Google Scholar 

  6. 6.

    Tzafestas S (1997) Concerning automated assembly: knowledge-based issues and a fuzzy system for assembly under uncertainty. Comput Integr Manuf Syst 10(3):183–192

    Article  Google Scholar 

  7. 7.

    McNaught K, Chan A (2011) Bayesian networks in manufacturing. J Manuf Technol Manag 22(6):734–747

    Article  Google Scholar 

  8. 8.

    Shi J (2006) Stream of variation modeling and analysis for multistage manufacturing processes. CRC Press/Taylor & Francis

  9. 9.

    Xie K, Wells L, Camelio JA, Youn BD (2007) Variation propagation analysis on compliant assemblies considering contact interaction. J Manuf Sci Eng 129:934

    Article  Google Scholar 

  10. 10.

    Jin S, Liu Y, Lin Z (2011) A Bayesian network approach for fixture fault diagnosis in launch of the assembly process. Int J Prod Res 50(23):6655–6666

    Article  Google Scholar 

  11. 11.

    Khanafer RM, Solana B, Triola J, Barco R, Moltsen L, Altman Z, Lazaro P (2008) Automated diagnosis for UMTS networks using Bayesian network approach. IEEE Trans Veh Technol 57(4):2451–2461

    Article  Google Scholar 

  12. 12.

    Dey S, Stori J (2005) A Bayesian network approach to root cause diagnosis of process variations. Int J Mach Tools Manuf 45(1):75–91

    Article  Google Scholar 

  13. 13.

    Mengshoel OJ, Chavira M, Cascio K, Darwiche A, Poll S, Uckun S (2010) Probabilistic model-based diagnosis: an electrical power system case study. IEEE Trans Syst Man Cybern Syst Hum 40(5):874–885

    Article  Google Scholar 

  14. 14.

    Lewis R, Ransing R (1997) A semantically constrained Bayesian network for manufacturing diagnosis. Int J Prod Res 35(8):2171–2188

    Article  MATH  Google Scholar 

  15. 15.

    Sayed MS, Lohse N (2013) Distributed Bayesian diagnosis for modular assembly systems—A case study. J Manuf Syst 32(3): 480–488

  16. 16.

    Jeong I, Leon V, Villalobos J (2007) Integrated decision-support system for diagnosis, maintenance planning, and scheduling of manufacturing systems. Int J Prod Res 45(2):267–285

    Article  MATH  Google Scholar 

  17. 17.

    Korb KB, Nicholson AE (2004) Bayesian artificial intelligence, vol 1. CRC press

  18. 18.

    Neil M, Fenton N, Nielson L (2000) Building large-scale Bayesian networks. Knowl Eng Rev 15(3):257–284

    Article  MATH  Google Scholar 

  19. 19.

    Koller D, Pfeffer A Object-oriented Bayesian networks. In: Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, 1997. Morgan Kaufmann Publishers Inc., pp 302–313

  20. 20.

    Roychoudhury I, Biswas G, Koutsoukos X (2009) Designing distributed diagnosers for complex continuous systems. IEEE Trans Autom Sci Eng 6(2):277–290

    Article  Google Scholar 

  21. 21.

    Ding Z, Peng Y A probabilistic extension to ontology language OWL. In: System Sciences, 2004. Proceedings of the 37th Annual Hawaii international conference on, 2004. IEEE, p 10 pp

  22. 22.

    van der Gaag LC, Tabachneck-Schijf HJ (2010) Library-style ontologies to support varying model views. Int J Approx Reason 51(2):196–208

    Article  Google Scholar 

  23. 23.

    Helsper EM, van der Gaag LC Building Bayesian networks through ontologies. In: Proceedings of the 15th European Conference on Artificial Intelligence, ECAI'2002, Lyon, France, July 2002

  24. 24.

    Devitt A, Danev B, Matusikova K (2006) Constructing Bayesian networks automatically using ontologies. Paper presented at the Second Workshop on Formal Ontologies Meets Industry Trento, Italy, 2006

  25. 25.

    Fenz S (2012) An ontology-based approach for constructing Bayesian networks. Data Knowl Eng 73:73–88

    Article  Google Scholar 

  26. 26.

    Dittmann L, Rademacher T, Zelewski S Combining knowledge management and quality management systems. In: Vortrag anlässlich: 48th European Organization for Quality Congress (EOQ 04) in Moskau, GUS (08.09. 2004), 2004. Citeseer

  27. 27.

    Lascelles D, Dale B (1988) A study of the quality management methods employed by UK automotive suppliers. Qual Reliab Eng Int 4(4):301–309

    Article  Google Scholar 

  28. 28.

    Ebrahimipour V, Rezaie K, Shokravi S (2010) An ontology approach to support FMEA studies. Expert Syst Appl 37(1):671–677

    Article  Google Scholar 

  29. 29.

    Dittmann L, Rademacher T, Zelewski S (2004) Performing FMEA using ontologies. In: The 18th international workshop on qualitative reasoning, Northwestern University, Evanston, Illinois, USA

  30. 30.

    Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann

  31. 31.

    Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs. Springer, Verlag

    Google Scholar 

  32. 32.

    Lohse N (2006) Towards an ontology framework for the integrated design of modular assembly systems, PhD Thesis, University of Nottingham

  33. 33.

    Kim K-Y, Manley DG, Yang H (2006) Ontology-based assembly design and information sharing for collaborative product development. Comput Aided Des 38(12):1233–1250

    Article  Google Scholar 

  34. 34.

    Lastra JLM, Delamer IM (2009) Ontologies for production automation. In: Advances in Web Semantics I. Springer, pp 276–289

  35. 35.

    Lin H, Harding JA (2007) A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration. Comput Ind 58(5):428–437

    Article  Google Scholar 

  36. 36.

    Kusiak A (1999) Engineering design: products, processes, and systems. Academic Press, Inc

  37. 37.

    Chen D, Kjellberg T, von Euler A Software Tools for the Digital Factory–An Evaluation and Discussion. In: Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology, 2010. Springer, pp 803–812

  38. 38.

    Butterfield J, Ng I, Roy R, McEwan W Enabling value co-production in the provision of support service engineering solutions using digital manufacturing methods. In: Simulation Conference (WSC), Proceedings of the 2009 Winter, 2009. IEEE, pp 3009–3022

  39. 39.

    Carlson C, Sarakakis G, Groebel D, Mettas A Best practices for effective reliability program plans. In: Reliability and Maintainability Symposium (RAMS), 2010 Proceedings-Annual, 2010. IEEE, pp 1–7

  40. 40.

    FRAME (2012) Fast ramp-up and adaptive manufacturing environment. www.frame-eu.org

  41. 41.

    Woodberry O, Nicholson AE, Korb KB, Pollino C (2005) Parameterising bayesian networks. In: AI 2004: Advances in Artificial Intelligence. Springer, pp 1101–1107

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Correspondence to Mohamed S. Sayed.



Table 3 CPT of VisionTest
Table 4 CPT of LeakTest
Table 5 CPT of ComponentsCompleteness
Table 6 CPT of JointQuality
Table 7 CPTs of PartOrientation, ProcessTime2 and ProcessTime

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Sayed, M.S., Lohse, N. Ontology-driven generation of Bayesian diagnostic models for assembly systems. Int J Adv Manuf Technol 74, 1033–1052 (2014). https://doi.org/10.1007/s00170-014-5918-0

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  • Assembly
  • Modular design
  • Bayesian networks
  • Error diagnosis
  • Multi-agent systems