0
Research Papers

Narrower System Reliability Bounds With Incomplete Component Information and Stochastic Process Loading

[+] Author and Article Information
Yao Cheng

Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
258A Toomey Hall,
400 West 13th Street,
Rolla, MO 65409-0500
e-mail: ycbm7@mst.edu

Daniel C. Conrad

Design Quality, Reliability, and Testing,
Hussmann Corporation,
12999 Saint Charles Rock Road,
Bridgeton, MO 63044
e-mail: dan.conrad@hussmann.com

Xiaoping Du

Professor
Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
272 Toomey Hall,
400 West 13th Street,
Rolla, MO 65409-0500
e-mail: dux@mst.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received April 22, 2016; final manuscript received December 7, 2016; published online May 16, 2017. Assoc. Editor: Jitesh H. Panchal.

J. Comput. Inf. Sci. Eng 17(4), 041007 (May 16, 2017) (11 pages) Paper No: JCISE-16-1938; doi: 10.1115/1.4035530 History: Received April 22, 2016; Revised December 07, 2016

Incomplete component information may lead to wide bounds for system reliability prediction, making decisions difficult in the system design stage. The missing information is often the component dependence, which is a crucial source for the exact system reliability estimation. Component dependence exists due to the shared environment and operating conditions. But it is difficult for system designers to model component dependence because they may have limited information about component design details if outside suppliers designed and manufactured the components. This research intends to produce narrow system reliability bounds with a new way for system designers to consider the component dependence implicitly and automatically without knowing component design details. The proposed method is applicable for a wide range of applications where the time-dependent system stochastic load is shared by components of the system. Simulation is used to obtain the extreme value of the system load for a given period of time, and optimization is employed to estimate the system reliability bounds, which are narrower than those from the traditional method with independent component assumption and completely dependent component assumption. Examples are provided to demonstrate the proposed method.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 2

Flowchart of the proposed methodology

Grahic Jump Location
Fig. 3

Simplified free-body diagram of component i

Grahic Jump Location
Fig. 5

Five identical components sharing same load

Grahic Jump Location
Fig. 6

CDFs of maximum load Lmax in Example 1

Grahic Jump Location
Fig. 7

Probability of component failure with respect to time

Grahic Jump Location
Fig. 8

Bounds contrast from traditional and proposed methods

Grahic Jump Location
Fig. 9

System configuration

Grahic Jump Location
Fig. 10

CDFs of maximum load Lmax in Example 2

Grahic Jump Location
Fig. 11

Probabilities of component failure with respect to time

Grahic Jump Location
Fig. 12

Bounds contrast from traditional and proposed methods

Tables

Errata

Discussions

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