A Bayesian Approach to Reliability-Based Optimization With Incomplete Information

[+] Author and Article Information
Subroto Gunawan

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109broto@umich.edu

Panos Y. Papalambros

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109

J. Mech. Des 128(4), 909-918 (Jan 25, 2006) (10 pages) doi:10.1115/1.2204969 History: Received September 15, 2005; Revised January 25, 2006

In engineering design, information regarding the uncertain variables or parameters is usually in the form of finite samples. Existing methods in optimal design under uncertainty cannot handle this form of incomplete information; they have to either discard some valuable information or postulate existence of additional information. In this article, we present a reliability-based optimization method that is applicable when information of the uncertain variables or parameters is in the form of both finite samples and probability distributions. The method adopts a Bayesian binomial inference technique to estimate reliability, and uses this estimate to maximize the confidence that the design will meet or exceed a target reliability. The method produces a set of Pareto trade-off designs instead of a single design, reflecting the levels of confidence about a design’s reliability given certain incomplete information. As a demonstration, we apply the method to design an optimal piston-ring/cylinder-liner assembly under surface roughness uncertainty.

Copyright © 2006 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 9

Pareto optima for different sample numbers N

Grahic Jump Location
Figure 1

Uncertainty quantification based on information content

Grahic Jump Location
Figure 2

Beta-distributed Fgj(0) estimate for different r(N=10)

Grahic Jump Location
Figure 3

Feasible-infeasible realization of a (Xs,Ps) sample given (Xt,Pt) pdf’s

Grahic Jump Location
Figure 4

Probability distribution of X1

Grahic Jump Location
Figure 5

Actual and estimate reliability of (μX1,μX2)=(−2,0.5)

Grahic Jump Location
Figure 6

Beta distribution of Fg(0) for increasing N

Grahic Jump Location
Figure 7

The N-R-ζsmax diagram; (a) ζsmax as ordinate, (b) N as ordinate

Grahic Jump Location
Figure 8

Probability distributions of X1

Grahic Jump Location
Figure 10

Pareto optima in the design variable space

Grahic Jump Location
Figure 11

Optima obtained by fitting a Gaussian distribution to X1 samples

Grahic Jump Location
Figure 12

The piston-ring/cylinder-liner model

Grahic Jump Location
Figure 13

Pareto optima of the ring-liner design problem




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In