Unified Reliability Measure Method Considering Uncertainties of Input Variables and Their Distribution Parameters
Source abstract
Abstract
Aleatoric and epistemic uncertainties can be represented probabilistically in mechanical systems. However, the distribution parameters of epistemic uncertainties are also uncertain due to sparsely available or inaccurate uncertainty information. Therefore, a unified reliability measure method that considers uncertainties of input variables and their distribution parameters simultaneously is proposed. The uncertainty information for distribution parameters of epistemic uncertainties could be as a result of insufficient data or interval information, which is represented with evidence theory. The probability density function of uncertain distribution parameters is constructed through fusing insufficient data and interval information based on a Gaussian interpolation algorithm, and the epistemic uncertainties are represented using a weighted sum of probability variables based on discrete distribution parameters. The reliability index considering aleatoric and epistemic uncertainties is calculated around the most probable point. The effectiveness of the proposed algorithm is demonstrated through comparison with the Monte Carlo method in the engineering example of a crank-slider mechanism and composite laminated plate.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Probabilistic and Robust Engineering Design · Fatigue and fracture mechanics · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310027, China
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Funding
National Natural Science Foundation of China · Zhejiang Provincial Natural Science Foundation of China
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