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Research Papers

Dependability-Based Design Optimization of Degrading Engineering Systems

[+] Author and Article Information
Gordon J. Savage

Systems Design Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canadagjsavage@uwaterloo.ca

Young Kap Son

School of Mechanical Engineering, Andong National University, 388 Seongcheon-dong, Andong-si, Gyeongsangbuk-do 760-759, South Koreaykson@andong.ac.kr

J. Mech. Des 131(1), 011002 (Dec 11, 2008) (10 pages) doi:10.1115/1.3013295 History: Received January 07, 2008; Revised August 05, 2008; Published December 11, 2008

In this paper, we present a methodology that helps select the distribution parameters in degrading multiresponse systems to improve dependability at the lowest lifetime cost. The dependability measures include both quality (soft failures) and reliability (hard failures). Associated costs of scrap, rework, and warrantee work are included. The key to the approach is the fast and efficient creation of the system cumulative distribution function through a series of time-variant limit-state functions. Probabilities are evaluated by Monte Carlo simulation although the first-order reliability method is a viable alternative. The cost objective function that is common in reliability-based design optimization is expanded to include a lifetime loss of performance cost, herein based on present worth theory (also called present value theory). An optimum design in terms of distribution parameters of the design variables is found via a methodology that involves minimizing cost under performance policy constraints over the lifetime as the system degrades. A case study of an over-run clutch provides the insights and potential of the proposed methodology.

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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Component degradations leading to performance degradations and to system performance reliability (modified from Ref. 7)

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Figure 2

Trajectories of a limit-state surface over time: (a) single degradation path and (b) multiple degradation paths

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Figure 3

Schematic of over-run clutch

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Figure 4

Time-variant limit-state surfaces at times 0 and 10 yr in 2D space

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Figure 5

The errors ε1(tl,tl+1) over time with respect to sample sizes

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Figure 6

Cdf for hard failure, soft failure, and system failure

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Figure 7

Cumulative distribution functions for four designs

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