Analyzing reliability and sensitivity to reliability of rescue attachment with multiple failure modes

Wang, Chunrong; Zhao, Jingjin; Xia, Erdong; Gao, Hao

doi:10.1007/s40430-021-02856-2

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…We now verify the performance of the proposed IPSO-MCopula model through the example of a hollow metal strut subjected to forces at two ends 3 (as shown in Figure 3) where the failure functions of the stability failure mode ( g 1 ) and the strength failure mode ( g 2 ) are respectively expressed as:…”

Section: Resultsmentioning

confidence: 99%

“…To optimally determine the parameters appeared in the proposed MCopula function, we firstly use the Monte Carlo (MC) scheme to randomly generate the sample data and the MLE scheme to estimate the dependent parameters, where both schemes were quite successfully applied in reliability analysis problems. 3,33,34 Secondly, the weight coefficient is determined by the IPSO algorithm proposed by this study. Details are described as follows.…”

Section: Parameters Estimationmentioning

confidence: 99%

“…That means certain degrees of correlations exist among failure modes. [2][3][4][5][6][7] In the past, many scholars studied the reliability as well as the failure mode problems of mechanic parts. [8][9][10][11][12][13][14][15] Ditlevsen 8 first proposed the narrow bound method for system reliability calculations using some widely accepted failure mode correlations.…”

Section: Introductionmentioning

confidence: 99%

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A novel reliability analysis methodology based on IPSO-MCopula model for gears with multiple failure modes

ErDong,

FuPing,

Kun-Chieh

et al. 2024

Advances in Mechanical Engineering

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In order to accurately and quickly predict the failure probability of gears with multiple failure modes, a novel reliability analysis methodology based on the mixed Copula (MCopula) function model is proposed to deal with the complex correlation among different failure functions. Firstly, we construct a novel MCopula model based on three famous Copula functions: Gumbel Copula, Clayton Copula, and Frank Copula functions. Secondly, we use and improve the particle swarm optimization (PSO) method to optimally calculate the weight coefficients in the proposed MCopula model. Thirdly, the maximum likelihood estimation (MLE) method is adopted to estimate related parameters in the proposed MCopula model. Finally, we verify the proposed reliability analysis methodology with a standard life-prediction case of a strut system and a practical life-problem case of a gear pair system. Comparison results of both cases show that, by using the proposed methodology, the failure probability of a gear pair system with multiple failure correlations can be quickly calculated through a small number of samples and can be estimated as accurately as that by the Monte Carlo scheme. Consequently, our proposed novel methodology successfully analyzes the reliability problems for a gear pair system with multiple failure modes. The proposed methodology can be further applied to solve the reliability problem for other machine parts.

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