Reliability Modeling and Evaluation of Compression System Considering Multiple Factors Influence

易春兰; 张斌; 宋宇飞; 牟晨; 胡嘉蕊

doi:10.13433/j.cnki.1003-8728.20230363

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Reliability Modeling and Evaluation of Compression System Considering Multiple Factors Influence

更新时间：2026-04-23

- Reliability Modeling and Evaluation of Compression System Considering Multiple Factors Influence
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 10, Pages: 1793-1800(2025)
- 作者机构：
  
  航空工业成都飞机工业(集团)有限责任公司,成都,610073
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230363
  CLC：
- Published：2025
- 稿件说明：
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易春兰, 张斌, 宋宇飞, et al. Reliability Modeling and Evaluation of Compression System Considering Multiple Factors Influence[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(10): 1793-1800.
DOI：

易春兰, 张斌, 宋宇飞, et al. Reliability Modeling and Evaluation of Compression System Considering Multiple Factors Influence[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(10): 1793-1800. DOI： 10.13433/j.cnki.1003-8728.20230363.

摘要

随着现代大型机电系统的智能化、数字化和集成化程度不断提高

传统的可靠性建模与分析技术已不能满足以认知不确定性及相关失效为特点的现代复杂系统的评估需求。将区间分析方法与贝叶斯网络结合

建立区间贝叶斯网络

采用区间的形式对部件失效率进行描述; 在区间贝叶斯网络框架下

融合Copula理论对系统中的相关失效问题进行分析

从而提出基于区间贝叶斯网络的复杂系统可靠性分析与评估方法。最后

将该方法应用于某压缩机压缩系统的可靠性建模与评估中; 通过对实际系统的分析

表明建立的多因素影响下系统可靠性分析方法的有效性。

Abstract

With the increasing degree of intelligence

digitization and integration of modern large-scale electromechanical systems

traditional reliability modeling and analysis techniques can no longer meet the evaluation needs of modern complex systems characterized by epistemic uncertainty and related failures. Interval analysis method is combined with Bayesian network to establish an interval Bayesian network. In the Bayesian network

component failure rates are described in the form of intervals. Under the framework of interval Bayesian network

the Copula theory is fused to analyze the failure dependence in the system

and the reliability analysis and evaluation method of complex system based on interval Bayesian network is proposed. Finally

the method is applied to the reliability modeling and evaluation of a compression system. Through the analysis of the real system

the effectiveness of the system reliability analysis method under the influence of multiple factors is demonstrated.

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School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China

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