Integration of Statistics-And Physics-Based Methods-A Feasibility Study on Accurate System Reliability Prediction
Component reliability can be estimated by either statistics-based methods with data or physics-based methods with models. Both types of methods are usually independently applied, making it difficult to estimate the joint probability density of component states, which is a necessity for an accurate system reliability prediction. The objective of this study is to investigate the feasibility of integrating statistics-and physics-based methods for system reliability analysis. The proposed method employs the first-order reliability method (FORM) directly for a component whose reliability is estimated by a physics-based method. For a component whose reliability is estimated by a statistics-based method, the proposed method applies a supervised learning strategy through support vector machines (SVM) to infer a linear limit-state function that reveals the relationship between component states and basic random variables. With the integration of statistics-and physics-based methods, the limit-state functions of all the components in the system will then be available. As a result, it is possible to predict the system reliability accurately with all the limit-state functions obtained from both statistics-and physics-based reliability methods.
Z. Hu and X. Du, "Integration of Statistics-And Physics-Based Methods-A Feasibility Study on Accurate System Reliability Prediction," Journal of Mechanical Design, Transactions of the ASME, vol. 140, no. 7, American Society of Mechanical Engineers (ASME), Jul 2018.
The definitive version is available at https://doi.org/10.1115/1.4039770
Mechanical and Aerospace Engineering
Keywords and Phrases
Forecasting; Probability Density Function; Statistics; Structural Analysis; Support Vector Machines, Component Reliability; Feasibility Studies; First Order Reliability Methods; Joint Probability; Limit State Functions; Physics-Based Methods; Reliability Methods; System Reliability, Reliability Analysis
International Standard Serial Number (ISSN)
Article - Journal
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