System Reliability Analysis With Autocorrelated Kriging Predictions

Wu, Hao; Zhu, Zhifu; Du, Xiaoping

doi:10.1115/1.4046648

Cited by 49 publications

(9 citation statements)

References 38 publications

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“…This example involves a black-box FEM model. For design problems that need computationally expensive models, we can at first create cheaper surrogate models [45][46][47][48] to replace the original models, and then use the proposed approach based on the surrogate models.…”

Section: Md-20-1572 Dumentioning

confidence: 99%

A Safety Factor Method for Reliability-Based Component Design

Yin

2021

Journal of Mechanical Design

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Reliability-based design (RBD) identifies design variables that maintain reliability at a required level. For many routine component design jobs, RBD may not be practical as it requires nonlinear optimization and specific reliability methods, especially for those design jobs which are performed manually or with a spreadsheet. This work develops a practical approach to reliability-based component design so that the reliability target can be achieved by conducting traditional component design repeatedly using a deterministic safety factor. The new component design is based on the First Order Reliability Method, which iteratively assigns the safety factor during the design process until the reliability requirement is satisfied. In addition to several iterations of deterministic component design, the other additional work is the calculation of the derivatives of the design margin with respect to the random input variables. The proposed method can be used for a wide range of component design applications. For example, if a deterministic component design is performed manually or with a spreadsheet, so is the reliability-based component design. Three examples are used to demonstrate the practicality of the new design method.

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Section: Md-20-1572 Dumentioning

confidence: 99%

A Safety Factor Method for Reliability-Based Component Design

Yin

2021

Journal of Mechanical Design

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“…Physics-based reliability methods can be divided into three categories: numerical methods [1][2][3][4][5], surrogate methods [6][7][8][9][10][11], and simulation methods [12][13][14][15]. Typically, numerical methods simplify the limit-state function using the first or second order Taylor expansion.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Reliability Method Accounting for Important and Unimportant Input Variables

Yin

2021

Journal of Mechanical Design

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Reliability analysis is usually a core element in engineering design and can be performed with physical models (limit-state functions). Reliability analysis becomes computationally expensive when the dimensionality of input random variables is high. This work develops a high dimensional reliability analysis method by a new dimension reduction strategy so that the contributions of unimportant input variables are also accommodated by the dimension reduction. The dimension reduction is performed with the first iteration of the first order reliability method (FORM), which identifies important and unimportant input variables. Then a higher order reliability analysis is performed in the reduced space of only important input variables. The reliability obtained in the reduced space is then integrated with the contributions of unimportant input variables, resulting in the final reliability prediction that accounts for both types of input variables. Consequently, the new reliability method is more accurate than the traditional method, which fixes unimportant input variables at their means. The accuracy is demonstrated by three examples.

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“…Physics-based methods for component reliability have also been extensively investigated. The most widely used component reliability methods include the First Order Reliability Method (FORM) [8][9][10], the Second Order Reliability (SORM) [11][12][13], Monte Carlo simulation (MCS) methods [14,15], Saddlepoint approximations (SPA) [16][17][18][19], and metamodeling methods [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Physics-based component reliability methods can be easily extended to system reliability analysis when all component limit-_________________________________________________________________________ This is the author's manuscript of the article published in final edited form as: state functions are available. In principle, the joint PDF of all the component states can be derived from the limit-state functions by FORM, SORM, SPA, MCS, and other methods [2,7,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data

2021

Volume 3B: 47th Design Automation Conference (DAC)

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Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the proposed method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.

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System Reliability Analysis With Autocorrelated Kriging Predictions

Cited by 49 publications

References 38 publications

A Safety Factor Method for Reliability-Based Component Design

A Safety Factor Method for Reliability-Based Component Design

High-Dimensional Reliability Method Accounting for Important and Unimportant Input Variables

A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data

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