Hostname: page-component-84b7d79bbc-4hvwz Total loading time: 0 Render date: 2024-07-26T04:25:21.788Z Has data issue: false hasContentIssue false
  • English
  • Français

Computer-aided design/engineering of bearing systems using the Dempster-Shafer theory

Published online by Cambridge University Press:  27 February 2009

A.C. Butler ,
F. Sadeghi ,
S.S. Rao  and
S.R. LeClair
A.C. Butler
Affiliation:
 Mechanical Engineering Department, Purdue University, W. Lafayette, IN 47907-1288, USA
F. Sadeghi
Affiliation:
 Mechanical Engineering Department, Purdue University, W. Lafayette, IN 47907-1288, USA
S.S. Rao
Affiliation:
 Mechanical Engineering Department, Purdue University, W. Lafayette, IN 47907-1288, USA
S.R. LeClair
Affiliation:
 U.S. Air Force Materials Directorate, WL/MLIM, Wright-Patterson AFB, OH 45433USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Research in computer-aided design/engineering (CAD/E) has focused on enhancing the capability of computer systems in a design environment, and this work has continued in this trend by illustrating the use of the Dempster-Shafer theory to expand the computer’s role in a CAD/E environment. An expert system was created using Dempster-Shafer methods that effectively modeled the professional judgment of a skilled tribologist in the selection of rolling element bearings. A qualitative and symbolic approach was used, but access to simple quantitative models was provided to the expert system shell. Although there has been significant discussion in the literature regarding modification/improvement of the Dempster-Shafer theory, Shafer’s theories were found adequate in all respects for replicating the expert’s judgment. However, an understanding of the basic theory is required for interpreting the results.

Keywords

Dempster-Shafer TheoryDesign of Bearing SystemsExpert Systems
Type
Articles
Information
AI EDAM , Volume 9 , Issue 1 , January 1995 , pp. 1 - 11
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alim, S. (1988). Application of Dempster-Shafer theory for interpretation of seismic parameters. J. Struct. Eng. 114(9), 2070–2084.CrossRefGoogle Scholar
Barnett, J.A. (1981). Computational methods for a mathematical theory of evidence. Proc. IJCAI, 868–875.Google Scholar
Biswas, G., Abramczyk, R., & Oliff, M. (1987). OASES: An expert system for operations analysis–The system for cause analysis. IEEE Trans. Syst. Man Cybernet. SMC17(2), 133–145.CrossRefGoogle Scholar
Brulé, J.F., & Blount, A. (1989). Knowledge Acquisition. McGraw-Hill, New York.Google Scholar
Butler, A.C. (1992). A Study of New Techniques for Managing Conflict and Uncertainty in Computer Aided Design and Engineering (CAD/E). Ph.D. Dissertation, Purdue University, W. Lafayette, Indiana.Google Scholar
Cortes-Rello, E., & Golshani, F. (1990). Uncertain reasoning using the Dempster-Shafer method: An application in forecasting and marketing management. Expert Syst. 7(1), 9–17.CrossRefGoogle Scholar
Dempster, A.P. (1967). Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Statist. 38, 325–339.CrossRefGoogle Scholar
Dillard, R.A. (1992). Using data quality measures in decision-making algorithms. IEEE Expert 7(6), 63–72.CrossRefGoogle Scholar
Dillard, R.A. (1982a). The Dempster-Shafer theory applied to tactical data fusion in an inference system. In Proc. Fifth MIT/ONR Workshop.Google Scholar
Dillard, R.A. (1982b). Computing probability masses in rule-based systems. Naval Ocean Systems Center (NOSC) Technical Document 545. Naval Ocean Systems Center, San Diego, California.Google Scholar
Dillard, R.A. (1983). Computing confidences in tactical rule-based systems by using Dempster-Shafer theory. Naval Ocean Systems Center (NOSC), Technical Document 649, Naval Ocean Systems Center, San Diego, California.Google Scholar
Dong, W.-M., & Wong, F.S. (1987). Propagation of evidence in rule based systems. Int. J. Man-Machine Studies 26(5), 551–566.CrossRefGoogle Scholar
Fagan, M.J. (1987). Expert systems applied to mechanical engineering design–Experience with bearing selection and application program. Comput. Aided Design 19(7), 361–367.CrossRefGoogle Scholar
Galbraith, L., Al-Najjar, M., & Babu, A.J.G. (1988). Expert systems in engineering. IEEE Aerosp. Electron. Syst. Magazine 3(2), 12–14.CrossRefGoogle Scholar
Gordon, J., & Shortliffe, E.H. (1985). A method for managing evidential reasoning in a hierarchical hypothesis space. Artif. Intell. 26(3), 324–357.CrossRefGoogle Scholar
Hasle, G., Stokke, G., & Kloster, M. (1987). BETSY-An expert system for the selection of bearings. In Knowledge Based Expert Systems for Engineering: Classification, Education, and Control, pp. 221–237. Computational Mechanics Publications, London.Google Scholar
Hau, H.Y., & Kashyap, R.L. (1990). Belief combination and propagation in lattice-structured inference networks. IEEE Trans. Syst. Man Cybernet. 20(1), 45–58.CrossRefGoogle Scholar
Hayes-Roth, F., Waterman, D.A., & Lenat, D.B. (1983). Building Expert Systems, p. 153. Addison-Wesley Publishing Co., Reading, Massachusetts.Google Scholar
Hutchinson, S.A., Cromwell, R.L., & Kak, A.C. (1989). Applying uncertainty reasoning to model based object reasoning. In Proc. IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition, San Diego, California, pp. 541–548. IEEE, New York.Google Scholar
Kim, H., & Swain, P.H. (1989). Multisource data analysis in remote sensing and geographic information systems based on Shafer’s theory of evidence. In Proc. Twelfth Canad. Symp. Remote Sensing 2, Vancouver, pp. 829–832. IEEE, New York.Google Scholar
Lehrer, N.B., Reynolds, G., & Griffith, J. (1987). A method for initial hypothesis formation in image understanding. In Proc. First. Int. Conf. Comput. Vision, London, pp. 578–585. IEEE, New York.Google Scholar
Magee, K. (1987). The elicitation of knowledge from designers. Design Studies 8(2), 62–69.CrossRefGoogle Scholar
Meyer, M., & Booker, J. (1991). Eliciting and Analyzing Expert Judgement, a Practical Guide. Academic Press, London.Google Scholar
Morjaria, M. (1989). Knowledge-based systems for engineering design. SME Technical Paper No. MS89–693, Society of Manufacturing Engineers, Dearborn, Michigan.Google Scholar
Ng, K.-C., & Abramson, B. (1990). Uncertainty management in expert systems. IEEE Expert 5(2), 29–48.CrossRefGoogle Scholar
Qian, J., Ehrich, R.W., & Campbell, J.B. (1990). DNESYS–An expert system for automatic extraction of drainage networks from digital elevation data. IEEE Trans. Geosci. Remote Sensing 28(1), 29–45.CrossRefGoogle Scholar
Rychener, M.D. (1985). Expert systems for engineering design. Expert Systems 2(1), 30–44.CrossRefGoogle Scholar
Safranek, R.J., Gottschlich, S., & Kak, A.C. (1990). Evidence accumulation using binary frames of discernment for verification vision. IEEE Trans. Robotics Automat. 6(4), 405–417.CrossRefGoogle Scholar
Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press, Princeton, New Jersey.CrossRefGoogle Scholar
Shafer, G., & Logan, R. (1987). Implementing Dempster’s rule for hierarchical evidence. Artif. Intell. 33(3), 271–298.CrossRefGoogle Scholar
Stauffer, L.A., & Ullman, D.G. (1988). A comparison of the results of empirical studies into the mechanical design process. Design Studies 9(2), 107–114.CrossRefGoogle Scholar
Sim, S.K., & Chan, Y.W. (1991). A knowledge-based expert system for rolling element bearing selection in mechanical engineering design. Artif. Intell, in Eng. 6(3), 125–135.Google Scholar
Sy, B.K., & Deller, J.R. Jr (1989). An AI-based communication system for motor and speech disabled persons: Design methodology and prototype testing. IEEE Trans. Biomed. Eng. 36(5), 565–571.CrossRefGoogle ScholarPubMed
Yager, R.R. (1987). On the Dempster-Shafer framework and new combination rules. Inform. Sci. 41(2), 93–137.CrossRefGoogle Scholar
Yen, J. (1989). GERTIS: A Dempster-Shafer approach to diagnosing hierarchical hypotheses. Commun. ACM 32(5), 573–585.CrossRefGoogle Scholar
Zadeh, L.A. (1986). A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine 7(2), 85–90.Google Scholar