Hybrid reliability analysis of structural fatigue life: Based on Taylor expansion method

Meng, Guangwei; Feng, Xinyu; Zhou, Liming; Feng, Li

doi:10.1177/1687814016677023

Cited by 2 publications

(3 citation statements)

References 27 publications

(25 reference statements)

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“…However, it could not solve the problem where the interval boundary is the worst-case point. Meng et al 24 derived the first fourth-order central moments of the performance function using central tangent reduction and Taylor expansion methods to obtain the upper and lower bounds of structural failure probability. This method does not require the distribution of random variables and avoids the solution of the most probable failure point.…”

Section: Introductionmentioning

confidence: 99%

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Wen,

Hou,

Lin

et al. 2023

Quality & Reliability Eng

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The action responses of controlled mechanisms are often multiparametric, nonlinear, and uncertain. Complex dynamics and limited uncertain information pose difficulties for action reliability analysis. This paper develops an adaptive intelligent extremum surrogate model (AIESM) method for the action reliability under random‐interval hybrid uncertainty of a chain conveyor. First, a dynamic model of the chain conveyor is established, which considers the impact and frictional effects within the mechanical system, the regulating effects of the control system, and the external disturbances of the system. After that, a hybrid kernel extreme learning machine optimized by the sparrow search algorithm is employed as an intelligent surrogate model to construct the initial surrogate model from the hybrid uncertain variables to the limit state function (LSF) response and the extremum surrogate model (ESM) from the random variables to the LSF extremum response. An adaptive infilling strategy combining active learning and opposition‐based learning is applied to improve the accuracy and efficiency of the ESM and reduce the estimation error of action reliability. Finally, the action reliability interval bounds are obtained by Monte Carlo simulation based on the ESM. Two numerical examples are presented to illustrate the validity of the AIESM method. The action reliability interval of the chain conveyor provided by the proposed method is [0.9706, 0.9923].

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Section: Introductionmentioning

confidence: 99%

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Wen,

Hou,

Lin

et al. 2023

Quality & Reliability Eng

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show abstract

“…In this sense, although it is of great importance, the modeling of a fatigue phenomenon itself is a complex issue mainly due to the scarce experimental data available, which may lead to inadequate U design assumptions. Therefore, inherently to the nature of this phenomenon, variability is present, for example, in the following parameters: geometry, material properties, loading, and environmental conditions [2][3][4]. Consequently, the integration of fatigue analysis with uncertainty quantification (UQ) intends to partially fulfill the gap of investigating the influence of each uncertain input quantity (UIQ) in the required system response quantities (SRQs) of a fatigue problem.…”

Section: Introductionmentioning

confidence: 99%

“…Based on Bayes' theorem, more accurate inferences on SRQs may be reached by the available knowledge as the prior trustworthiness on model parameters. Finally, the probability bounds analysis (PBA), which is applied in [3][4]19]. This type of analysis, on which this paper is structured, is fundamentally related to: (a) Monte Carlo sampling (MCS) or a variation of it; and (b) evidence theory [4].…”

Section: Introductionmentioning

confidence: 99%

Bi-level Hybrid Uncertainty Quantification in Fatigue Analysis: S-N Curve Approach

Nonato

2020

Frattura Ed Integrità Strutturale

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Due to its physical complexity, fatigue phenomenon inherently presents a significant number of uncertain parameters to be predicted. In uncertainty quantification (UQ), research has demonstrated that even a small variation in uncertain input quantities (UIQs) may lead to a wide dispersion in the system response quantities (SRQs). In this paper, a bi-level hybrid UQ analysis of a fatigue problem is presented based on the S-N curve approach. The uncertain fatigue analysis presented is able to deal simultaneously with aleatory- and epistemic-type uncertainties in two levels (a SRQ in the first level is a UIQ in the second level). To this end, the proposed scheme is tested for an AISI 4130 clamped beam subjected to a concentrated load, which material information comes from experiments reported in the literature. The UIQs are geometrical parameters, material properties, loading magnitude, and stress, while the SRQs are the stress (which is also a UIQ for fatigue life) and fatigue life. The results evidenced that the uncertain fatigue analysis, instead of providing a unique value for a SRQ, now produces a possible range of values. Therefore, depending on the risk an engineer can take on a design, there will be a corresponding level of optimization achieved.

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Hybrid reliability analysis of structural fatigue life: Based on Taylor expansion method

Cited by 2 publications

References 27 publications

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Action reliability analysis under random‐interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

Bi-level Hybrid Uncertainty Quantification in Fatigue Analysis: S-N Curve Approach

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