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A novel approach for failure modes and effects analysis based on polychromatic sets

Published online by Cambridge University Press:  13 November 2008

Guo Li ,
Jianmin Gao  and
Fumin Chen
Guo Li
Affiliation:
State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
Jianmin Gao
Affiliation:
State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
Fumin Chen
Affiliation:
State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an, People's Republic of China
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Abstract

Traditional failure modes and effects analysis (FMEA) methods lack sufficient semantics and structure to provide full traceability between the failure modes and the effects of the complex system. To overcome this limitation, this paper proposes a formal failure knowledge representation model combined with the structural decomposition of the complex system. The model defines the failure modes as the inherent properties of the physical entities at different hierarchical levels, and employs the individual color, unified color, and Boolean matrix of the polychromatic sets to represent the failure modes in terms of their interrelationships and their relations to the physical system. This method is a structure-based modeling technique that provides a simple, yet comprehensive framework to organize the failure modes and their causes and effects more systematically and completely. Using the iterative search process operated on the reasoning matrices, the end effects on the entire system can be achieved automatically, which allows for the consideration of both the single and multiple failures. An example is embedded in the description of the methodology for better understanding. Because of the powerful mathematical modeling capability of the polychromatic sets, the approach presented in this paper makes significant progress in FMEA formalization.

Keywords

Causal RelationshipFailure Modes and Effects AnalysisKnowledge ModelingPolychromatic SetsReasoning Matrix
Type
Research Article
Information
AI EDAM , Volume 23 , Issue 2 , May 2009 , pp. 119 - 129
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Bell, D., Cox, L., Jackson, S., & Schaefer, P. (1992). Using causal reasoning for automated failure modes and effects analysis (FMEA). Proc. Annual Reliability and Maintainability Symp., pp. 343–353. Piscataway, NJ: IEEE.Google Scholar
Bluvband, Z., Polak, R., & Grabov, P. (2005). Bouncing failure analysis (BFA): the unified FTA-FMEA methodology. Proc. Annual Reliability and Maintainability Symp., pp. 463–467. Piscataway, NJ: IEEE.Google Scholar
Contini, S. (1995). A new hybrid method for fault tree analysis. Reliability Engineering and System Safety 49 ( 1), 13–21.CrossRefGoogle Scholar
Dean, A.W. (2003). A statistical evaluation of risk priority numbers in failure modes and effects analysis applied to the prediction of complex systems. PhD Dissertation, Old Dominion University.Google Scholar
Eubanks, C.F., Kmenta, S., & Ishii, K. (1996). System behavior modeling as a basis for advanced failure modes and effects analysis. Proc. 1996 ASME Design Engineering Technical Conf., pp. 1–8. New York: American Society of Mechanical Engineers.Google Scholar
Graham-Jones, P.J., & Mellor, B.G. (1995). Expert and knowledge-based systems in failure analysis. Engineering Failure Analysis 2 ( 2), 137–149.CrossRefGoogle Scholar
Gao, X.Q., & Li, Z.B. (2006). Computer aided conceptual design of mechanical product using polychromatic sets. 2006 IEEE Int. Conf. Mechatronics and Automation, pp. 1169–1174. Piscataway, NJ: IEEE Computer Society.Google Scholar
Hawkins, P.G., & Woollons, D.J. (1998). Failure modes and effects analysis of complex engineering systems using functional models. Artificial Intelligence in Engineering 12 ( 4), 375–397.CrossRefGoogle Scholar
Hu, W., Starr, A.G., & Leung, A.Y.T. (2003). Operational fault diagnosis of manufacturing systems. Journal of Materials Processing Technology 133(1–2), 108–117.CrossRefGoogle Scholar
Kara-Zaitri, C., Keller, A.Z., Barody, I., & Fleming, P.V. (1991). An improved FMEA methodology. Proc. Annual Reliability and Maintainability Symp., pp. 248–252. Piscataway, NJ: IEEE.Google Scholar
Lee, B.H. (2001). Using Bayes belief networks in industrial FMEA modeling and analysis. Proc. Annual Reliability and Maintainability Symp., pp. 7–15. Piscataway, NJ: IEEE.Google Scholar
Li, Z.B., & Xu, L.D. (2003). Polychromatic sets and its application in simulating complex objects and systems. Computers and Operations Research 30 ( 6), 851–860.CrossRefGoogle Scholar
Ormsby, A., Hunt, J., & Lee, M. (1991). Toward an automated FMEA assistant. Applications of Artificial Intelligence in Engineering VI, pp. 739–752. Southampton: Computational Mechanics.CrossRefGoogle Scholar
Price, C.J., Pugg, D.R., Snooke, N., Hunt, J.E., & Wilson, M.S. (1997). Combining functional and structural reasoning for safety analysis of electrical designs. Knowledge Engineering Review 12 ( 3), 271–287.CrossRefGoogle Scholar
Pavlov, V.V. (2000). Polychromatic Sets and Graphs for CALS. Moscow: Stankin Press.Google Scholar
Russomanno, D.J., Bonnell, R.D., & Bowles, J.B. (1993). Functional reasoning in a failure modes and effects analysis (FMEA) expert system. Proc. Annual Reliability and Maintainability Symp., pp. 339–347. Piscataway, NJ: IEEE.Google Scholar
Russomanno, D.J., Bonnell, R.D., & Bowles, J.B. (1994). Viewing computer-aided failure mode and effects analysis from an artificial intelligence perspective. Integrated Computer-Aided Design 1 ( 3), 209–228.CrossRefGoogle Scholar
Ruiz, I., Paniagua, E., Alberto, J., & Sanabria, J. (2000). State analysis: an alternative approach to FMEA, FTA and Markov analysis. Proc. Annual Reliability and Maintainability Symp., pp. 370–375. Piscataway, NJ: IEEE.Google Scholar
Sharma, R.K., Kumar, D., & Kumar, P. (2005). Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modeling. The International Journal of Quality & Reliability Management 22(8–9), 986–1004.CrossRefGoogle Scholar
Sun, Y., Ma, L., Mathew, J., & Zhang, S. (2006). An analytical model for interactive failures. Reliability Engineering and System Safety 91 ( 5), 495–504.CrossRefGoogle Scholar
Teoh, P.C., & Case, K. (2004). Failure modes and effects analysis through knowledge modeling. Journal of Materials Processing Technology 153–154, 253–260.CrossRefGoogle Scholar
Vandenbrande, W.W. (1998). How to use FMEA to reduce the size of your quality toolbox. Quality Progress 31 ( 11), 97–100.Google Scholar
Xu, L.D., Li, Z.B., Li, S.C., & Tang, F. (2005). A polychromatic sets approach to conceptual design of machine tools. International Journal of Production Research 43 ( 12), 2397–2421.CrossRefGoogle Scholar
Zhao, T.D., Su, T.J., He, X., & Sun, L.L. (2004). Intelligent FMEA based on model FIORN. Proc. Annual Reliability and Maintainability Symp., pp. 386–390. Piscataway, NJ: IEEE.Google Scholar