An improved first order approximate reliability analysis method for uncertain structures based on evidence theory

Liu, Xin; Gong, Min; Zhou, Zhenhua; Xie, Jun; Wu, Wenguang

doi:10.1080/15397734.2021.1956324

Cited by 19 publications

(8 citation statements)

References 51 publications

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“…Once a failure occurs, it is necessary to invest huge manpower and material resources in maintenance. With the development trend of higher and higher reliability requirements for mechanical products in various fields and smaller and smaller reliability test samples, the existing reliability evaluation methods have gradually failed to meet the engineering requirements on some occasions [11][12][13][14][15][16][17].…”

Section: Nomenclature G(s)mentioning

confidence: 99%

Dynamic Reliability Evaluation and Life Prediction of Transmission System of Multi-Performance Degraded Wind Turbine

Yuan¹,

Chen²,

Li³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Wind power is a kind of important green energy. Thus, wind turbines have been widely utilized around the world. Wind turbines are composed of many important components. Among these components, the failure rate of the transmission system is relatively high in wind turbines. It is because the components are subjected to aerodynamic loads for a long time. In addition, its inertial load will result in fatigue fracture, wear and other problems. In this situation, wind turbines have to be repaired at a higher cost. Moreover, the traditional reliability methods are difficult to deal with the above challenges when performing the reliability analysis of the transmission system of wind turbines. To solve this problem, a stress-strength interference model based on performance degradation is introduced. Based on considering the strength degradation of each component, the improved Monte Carlo method simulation based on the Back Propagation neural network is used to obtain the curve of system reliability over time. Finally, the Miner linear cumulative damage theory and the Carten-Dolan cumulative damage theory method are used to calculate the cumulative damage and fatigue life of the gear transmission system.

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Section: Nomenclature G(s)mentioning

confidence: 99%

Dynamic Reliability Evaluation and Life Prediction of Transmission System of Multi-Performance Degraded Wind Turbine

Yuan¹,

Chen²,

Li³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…In practical engineering, it is very difficult to directly solve the above integral. At present, the methods for approximately solving the above integrals are divided into two categories: (i) Analytical methods [10][11][12][13][14][15][16][17] and (ii) Simulation (sampling) approaches [18][19][20][21][22][23]. The first/second order reliability method (FORM/SORM) is one of the famous analytical methods.…”

Section: Introductionmentioning

confidence: 99%

A novel learning function for adaptive surrogate-model-based reliability evaluation

Yang,

Meng,

Wang

et al. 2023

Phil. Trans. R. Soc. A.

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The classical reliability analysis methods, due to the ever-increasing complexity of engineering structure, may lead to higher and higher calculation errors and costs. The adaptive surrogate-model-based reliability evaluation method strikes a desirable balance between computational efficiency and accuracy, making it a prevalent technique in the domain of reliability evaluation. Learning function is the core of this reliability evaluation method. In this study, a novel learning function is proposed to adaptively choose the best update sample. This learning function does not depend on the prediction variance provided by the Kriging model. Therefore, this learning function is not limited to the Kriging model. In theory, it can be combined with different surrogate models. Four comparative cases are used to illustrate the computational efficiency and accuracy of the proposed method, including series system case with four branches, highly nonlinear two-dimensional numerical example, and two practical engineering case. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

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“…These experiments or simulation are then often time-consuming, which will lead to high computational costs (Meng et al ., 2023b; Liu et al ., 2022; Xiao and Pedrycz, 2022; Zhu et al ., 2022). In reliability assessment, multi-physics coupling, multi-variable, multi-source uncertainty information (Wang et al ., 2022; Liu et al ., 2020; Pan and Deng, 2018; Xue and Deng, 2021) are often involved, so these assessments are even more unacceptable (Liu et al ., 2021b; Gao et al ., 2022; Yang et al ., 2022; Meng et al ., 2022b). Surrogate model (such as Kriging model (Li et al ., 2021; Meng et al ., 2021b), Canonical Low Rank Approximation (CLRA) (Wang et al ., 2019), Polynomial Chaos Expansions (PCE) (Zhu et al ., 2023), Support Vector Regression (SVR) (Luo et al ., 2022a) and Polynomial Chaos-Kriging (PCK) (Meng et al ., 2017; Schöbi et al ., 2017) etc.)…”

Section: Introductionmentioning

confidence: 99%

A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

Yang

Meng

Wang

et al. 2023

IJSI

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PurposeThis study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.Design/methodology/approachIn this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.FindingsBy comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.Originality/valueAlthough a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

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An improved first order approximate reliability analysis method for uncertain structures based on evidence theory

Cited by 19 publications

References 51 publications

Dynamic Reliability Evaluation and Life Prediction of Transmission System of Multi-Performance Degraded Wind Turbine

Dynamic Reliability Evaluation and Life Prediction of Transmission System of Multi-Performance Degraded Wind Turbine

A novel learning function for adaptive surrogate-model-based reliability evaluation

A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

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