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.


Mechanical and Aerospace Engineering

Research Center/Lab(s)

Intelligent Systems Center

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)

1050-0472; 1528-9001

Document Type

Article - Journal

Document Version


File Type





© 2018 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

01 Jul 2018