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Information fusion for decision support in manufacturing: studies from the defense sector


Information fusion, the synergistic combination of information from multiple sources, is an established research area within the defense sector. In manufacturing however, it is less well-established, with the exception of sensor/data fusion for automatic decision making. The paper briefly discusses some military specific models and methods for information fusion; analogies with manufacturing as well as a more generalized terminology are presented. “Manufacturing” is an application scenario within a Swedish information fusion research program that studies information fusion from databases, sensors and simulations with (currently) a focus on support for human decision making. An area of particular interest is that of advanced applications of virtual manufacturing such as synthetic environments, a form of hardware in the loop simulation that can deliver services such as service and maintenance at remote locations. In this area, the manufacturing industry can benefit from ongoing work in the defense sector related to verification, validation and accreditation of simulation models.

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  1. NSF (2006) Simulation based engineering science.

  2. PROSPEC (2004) THALES JP11.20 Report JP1120-WE5200-D5201-PROSPEC-V1.3.

  3. Dasarathy BV (2003) Information fusion as a tool in condition monitoring. Inf Fusion 4:71–73

    Article  Google Scholar 

  4. Kandilli I, Ertucc HM, Cakir B (2002) Real-time tool wear monitoring using neural networks. Mechatronics 2002, Univ. Twente, The Netherlands, pp 1018–1027

  5. Li B, Chow M-Y, Tipsuwan Y, Hung JC (2000) Neural-network based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069

    Article  Google Scholar 

  6. Salvan SE, Parkin RM, Coy J, Jackson MR, Li W (2002) Condition monitoring and Location of multiple roller bearings using three sensors. Mechatronics 2002, Univ. Twente, The Netherlands, pp 998–1007

  7. Carnero MC (2005) Selection of diagnostic techniques and instrumentation in a predictive maintenance program. A case study. Decis Support Syst 33(4):539–555

    Article  Google Scholar 

  8. Madan RN, Rao NSV (1999) Special issue on information/decision fusion with engineering applications. J Franklin Inst-Eng Applied Math 336(2):199–204

    Article  MATH  Google Scholar 

  9. Rao NSV (1997) Distributed decision fusion using empirical estimation. IEEE Trans Aerosp Electron Syst 33(4)1106–1114

    Article  Google Scholar 

  10. Ansari N, Chen JG, Zhang YZ (1007) Adaptive decision fusion for unequiprobable sources. IEE Proc Radar Sonar Navig 144(3):105–111

    Article  Google Scholar 

  11. Mirjalily G, Luo ZQ, Davidson TN, Bossé É (2003) Blind adaptive decision fusion for distributed detection. IEEE Trans Aerosp Electron Syst 39(1):34–52

    Article  Google Scholar 

  12. Demirbas K (1989) Distributed sensor data fusion with binary decision trees. IEEE Trans Aerosp Electron Syst 25(5):643–649

    Article  MathSciNet  Google Scholar 

  13. Rao NSV (1998) Vector space methods for sensor fusion problems. Opt Eng 37(2):499–504

    Article  Google Scholar 

  14. Telmoudi A, Chakhar S (2004) Data fusion application from evidential databases as a support for decision making. Info Soft Technol 46(8):547–555

    Article  Google Scholar 

  15. Dasarathy BV (2001) Information fusion - what, where, why, when, and how? Inf Fusion 2:75–76

    Article  Google Scholar 

  16. Bass T (2000) Intrusion detection systems and multisensor data fusion. Communications of the ACM, Vol. 43 Issue 4. ACM, New York, USA, pp 99–105

    Google Scholar 

  17. Warston H, Persson H (2004) Ground surveillance and fusion of ground target sensor data in a network based defense. Proc Fusion Stockholm, Sweden, pp 1195–1201

  18. Klein LA, Yi P, Teng HL (2002) Decision support system for advanced traffic management through data fusion. Transp Res Rec 1804:173–178

    Article  Google Scholar 

  19. McDaniel DM (2001) An information fusion framework for data integration. Thirteenth Annual Software Technology Conference “2001 Software Odyssey: Controlling Cost, Schedule, and Quality”, Salt Lake City, UT.

  20. MANVIS (2004) Presentation at MANUFUTURE conference, Enschede, The Netherlands.

  21. De Vin LJ, Moore PR, Pu J, Ng AHC, Steiner S, De Vicq A, Medland AJ (2002) ARMMS-A review of approaches to agile manufacturing, IMC-19 Conference, Belfast, pp 3–11

  22. Moore PR, Pu J, Ng AHC, Wong CB, Chen X, Adolfsson J, Olofsgård P, Lundgren JO (2003) Virtual engineering: an integrated approach to agile manufacturing machinery design and control. Mechatronics (Invited contribution for 10th Anniversary Special Issue) 10(13):1105–1121

    Google Scholar 

  23. Moore PR, Pu J, Wong C-B, Chong SK, Yang X (2003) A component-based development environment for life-cycle information management systems for consumer products. Proc International Conference on Computer, Communication and Control Technologies-CCCT ’03 (International Institute of Informatics & Systemics - I IIS). Orlando, Florida

  24. Chong SK, Pu J, Moore PR, Wong CB, Chen X, Ng AHC (2002) Component-based runtime support for agile modular manufacturing machinery. Mechatronics 1367–1376

  25. Solding P, Nilsson M, Eriksson P, De Vin LJ (2004) Structure for data management in simulation based planning activities. 37th CIRP International Seminar on Manufacturing Systems, Budapest, Hungary, pp 193–198

  26. Nilsson M, Solding P, De Vin LJ (2004) A system architecture for integrated simulation-based production planning and scheduling. Mechatronics. Ankara, Turkey, pp 815–824

  27. Sundberg M, Ng AHC, Adolfsson J, De Vin LJ (2006) Simulation supported service and maintenance in manufacturing, accepted for publication at IMC-23, Belfast, UK, Proceedings IMC-23 pp 559–566

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Correspondence to Leo J. De Vin.

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De Vin, L.J., Ng, A.H.C., Sundberg, M. et al. Information fusion for decision support in manufacturing: studies from the defense sector. Int J Adv Manuf Technol 35, 908–915 (2008).

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  • Decision support
  • Information fusion
  • Virtual manufacturing