Mechanical Structure Design Optimization by Blind Number Theory: Time-dependent Reliability
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Mechanical Structure Design Optimization by Blind Number Theory: Time-dependent Reliability

Authors: Zakari Yaou, Lirong Cui

Abstract:

In a product development process, understanding the functional behavior of the system, the role of components in achieving functions and failure modes if components/subsystem fails its required function will help develop appropriate design validation and verification program for reliability assessment. The integration of these three issues will help design and reliability engineers in identifying weak spots in design and planning future actions and testing program. This case study demonstrate the advantage of unascertained theory described in the subjective cognition uncertainty, and then applies blind number (BN) theory in describing the uncertainty of the mechanical system failure process and the same time used the same theory in bringing out another mechanical reliability system model. The practical calculations shows the BN Model embodied the characters of simply, small account of calculation but betterforecasting capability, which had the value of macroscopic discussion to some extent.

Keywords: Mechanical structure Design, time-dependent stochastic process, unascertained information, blind number theory.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057375

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1470

References:


[1] O. Ditlevsen, Stochastic Model for Joint Wave and Wind Loads on Offshore Structures, Structural Safety, vol. 24, no. 2-4, pp. 139-163, Apr. 2002.
[2] J. P. Li, G. Thompson, A Method to Take Account of Inhomogeneity in Mechanical Component Reliability calculations, IEEE Transaction on Reliability, vol. 54, no. 1, pp. 159C168, Mar. 2005.
[3] J. S. R. Jayaram, T. Girish, Reliability Prediction through Degradation Data Modeling using a Quasi-likelihood Approach, in Proc. Annu. Reliab. Maintainability Symp 2005, Alexandria, VA, United states, 2005, pp. 193-199.
[4] W. Huang, R. G. Askin, A Generalized SSI Reliability Model Considering Stochastic Loading and Strength Aging Degradation, IEEE Transactions on Reliability, vol. 53, no. 1, pp. 77C82, Mar. 2004.
[5] L. Ruzhong. Multi-agent Blind Model and its Application to Regional Eco-environmental Quality Assessmet. Chinese Geographical Science, 16(3), 2006.
[6] B. X. I. WAN Yu-cheng. The Analytic Hierarchy Process Based on the Numascertained Information. Beijing. Fuzzy Information Processing Theories and Applications(FIP2003).
[7] S. S. J. ZHANG Yong-Qiang. Software Reliability Modeling Based on Unascertained Theory. JOURNAL OF SOFTWARE, 17(8), 2006.
[8] H. H. CAI Liang, XIANG Tie-yuan. Blind-number model and indices for power generating system reliability assessment. Power System Technology, 27(8), 2003.
[9] W. H. Liu Kaidi. Mathematics processing and application of uncertain information. Science press, Beijing, 1999.