基于故障树与模糊贝叶斯网络的电抗器故障风险评估

刘亚乐; 马宏忠; 陈昱同; 刘华冬

doi:10.19666/j.rlfd.202509058

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基于故障树与模糊贝叶斯网络的电抗器故障风险评估

更新时间：2026-06-12

- 基于故障树与模糊贝叶斯网络的电抗器故障风险评估
- Thermal Power Generation Vol. 55, Issue 4, Pages: 175-182(2026)
- 作者机构：
  
  河海大学电气与动力工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202509058
  CLC： TM47;TP18
- Published：2026
- 稿件说明：
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[1]刘亚乐,马宏忠,陈昱同,等.基于故障树与模糊贝叶斯网络的电抗器故障风险评估[J].热力发电,2026,55(04):175-182.
[1]刘亚乐,马宏忠,陈昱同,等.基于故障树与模糊贝叶斯网络的电抗器故障风险评估[J].热力发电,2026,55(04):175-182. DOI： 10.19666/j.rlfd.202509058.

DOI：10.19666/j.rlfd.202509058.

摘要

针对电抗器故障风险评估中过度依赖主观经验的问题，提出一种基于故障树与模糊贝叶斯网络的综合评估方法。通过建立涵盖振动异常、温升异常和油色谱异常3类故障表现的故障树模型，梳理22项风险因子的因果逻辑，并将其转换为贝叶斯网络。采用梯形模糊数处理专家评价的语言不确定性，结合专家权重计算底事件概率，通过正向推理得到电抗器整体故障概率，利用逆向推理和故障重要度指标识别关键故障原因。实例分析表明，该方法能够精准评估故障风险，识别故障原因，并为运维决策提供依据。所提出的基于故障树与模糊贝叶斯网络的综合评估方法有效解决了电抗器故障风险评估中依赖主观经验的问题，具有较高的应用价值和广阔的应用前景。

Abstract

In view of the limitations of excessive reliance on subjective experience in reactor fault risk assessment

a comprehensive evaluation method based on fault tree and fuzzy Bayesian network is proposed. A fault tree model covering three fault manifestations

abnormal vibration

abnormal temperature rise

and abnormal oil chromatography

is built to clarify the causal logic of 22 risk factors and then mapped into a Bayesian network. Trapezoidal fuzzy numbers are used to handle linguistic uncertainty in expert judgment

and expert weights are combined to obtain basic event probabilities. Forward reasoning yields the overall reactor fault probability

while backward reasoning and fault importance indices are simultaneously involved to identify key fault causes. Case analysis shows that the method accurately assesses fault risk

identifies fault causes

and provides a basis for operation and maintenance decisions. The proposed comprehensive evaluation method based on fault tree and fuzzy Bayesian network effectively reduces subjective dependence in reactor fault risk assessment

offering high practical value and broad application prospects.

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references

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