Reduction of System Reliability Model Uncertainty by Considering Dependent Components with Stochastic Process Loading
When component dependence is ignored, a system reliability model may have large model (epistemic) uncertainty with wide reliability bounds. This makes decision making difficult during the system design. Component dependence exists due to the shared environment and operation conditions. It is difficult for system designers to model component dependence because they may not have access to component design details if the components are designed and manufactured by outside suppliers. This research intends to reduce the system reliability model uncertainty 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 interval. As a result, the epistemic uncertainty in system reliability can be reduced.
Y. Cheng and X. Du, "Reduction of System Reliability Model Uncertainty by Considering Dependent Components with Stochastic Process Loading," Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016, Charlotte, NC), vol. 1A-2016, American Society of Mechanical Engineers (ASME), Aug 2016.
The definitive version is available at http://dx.doi.org/10.1115/DETC2016-59260
ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016: Aug. 21-24, Charlotte, NC)
Mechanical and Aerospace Engineering
Keywords and Phrases
Decision making; Design; Random processes; Stochastic models; Stochastic systems; Systems analysis; Uncertainty analysis; Epistemic uncertainties; Model components; Operation conditions; Stochastic loads; System designers; System reliability; System reliability modeling; Time-dependent systems; Reliability
International Standard Book Number (ISBN)
Article - Conference proceedings
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