A probabilistic fatigue life prediction method under random combined high and low cycle fatigue load history

Bai, Song; Huang, Tudi; Li, Yanfeng; Lü, Ning; Huang, Hong‐Zhong

doi:10.1016/j.ress.2023.109452

Cited by 13 publications

(1 citation statement)

References 39 publications

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“…In practical applications of the Bayesian method for reliability estimation, a common challenge is the lack of comprehensive prior information. This issue is especially prevalent in assessing the reliability of new or upgraded industrial equipment, which often lacks extensive historical data [59][60][61][62]. A proven solution is to employ the Bayesian method to amalgamate diverse data types, including degradation and failure time data, recognized as multi-source heterogeneous data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Fusion of Degradation and Failure Time Data for Reliability Assessment of Industrial Equipment Considering Individual Differences

Fu,

Zhang,

et al. 2024

Processes

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In the field of industrial equipment reliability assessment, dependency on either degradation or failure time data is common. However, practical applications often reveal that single-type reliability data for certain industrial equipment are insufficient for a comprehensive assessment. This paper introduces a Bayesian-fusion-based methodology to enhance the reliability assessment of industrial equipment. Operating within the hierarchical Bayesian framework, the method innovatively combines the Wiener process with available degradation and failure time data. It further integrates a random effects model to capture individual differences among equipment units. The robustness and applicability of this proposed method are substantiated through an in-depth case study analysis.

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Section: Introductionmentioning

confidence: 99%

Bayesian Fusion of Degradation and Failure Time Data for Reliability Assessment of Industrial Equipment Considering Individual Differences

Fu,

Zhang,

et al. 2024

Processes

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An AK‐MCS‐based probabilistic fatigue life prediction framework for turbine disc with a mean stress correction model

Wang,

Li,

Huang

et al. 2024

Quality & Reliability Eng

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The turbine disc plays a crucial role in aerospace engine, significantly influencing both their fatigue life and overall reliability. Due to the substantial uncertainties present, devising a method for the probabilistic analysis of turbine disc fatigue life becomes crucial. This paper introduces a novel framework that combines adaptive Kriging Monte Carlo simulation (AK‐MCS) with a mean stress correction model to evaluate fatigue life distribution effectively. AK‐MCS, stands out as an algorithm that constructs a Kriging model, effectively reducing the need for an extensive finite element analysis. Furthermore, a mean stress correction model is proposed for the Masson‐Coffin equation tailored for fatigue life prediction of GH4133 disc material. This model has been empirically validated to be effective. The presented framework for probabilistic fatigue life analysis not only holds considerable engineering value but also offers innovative approaches for tackling analogous challenges in the field.

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A weakest link theory‐based probabilistic fatigue life prediction method for the turbine disc considering the influence of the number of critical sections

Wang,

Li,

Huang

et al. 2024

Quality & Reliability Eng

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This study utilizes the rank correlation coefficient to examine the multi‐site failure correlation of turbine discs. Drawing from the stress‐strength interference model, reliability models both with and without factoring in the multi‐site failure correlation are constructed. Furthermore, the weakest link theory (WLT) within the context of the Weibull distribution function is invoked to develop a model for predicting the fatigue life of turbine discs, taking into account the quantity of critical sections. The variability in the low cycle fatigue (LCF) of turbine discs is scrutinized, leading to the formulation of a probabilistic fatigue life prediction method for these discs. When comparing theoretical values with experimental ones, it becomes evident that factoring in the multi‐site failure correlation significantly enhances the accuracy of turbine disc life predictions.

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A probabilistic fatigue life prediction method under random combined high and low cycle fatigue load history

Cited by 13 publications

References 39 publications

Bayesian Fusion of Degradation and Failure Time Data for Reliability Assessment of Industrial Equipment Considering Individual Differences

Bayesian Fusion of Degradation and Failure Time Data for Reliability Assessment of Industrial Equipment Considering Individual Differences

An AK‐MCS‐based probabilistic fatigue life prediction framework for turbine disc with a mean stress correction model

A weakest link theory‐based probabilistic fatigue life prediction method for the turbine disc considering the influence of the number of critical sections

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