Fault diagnosis of mine hoist based on fuzzy fault tree and Bayesian network
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摘要: 为解决目前矿井提升机故障诊断方法效率低、准确性差等问题，提出了一种基于模糊故障树和贝叶斯网络的矿井提升机故障诊断方法。首先对传感器实时采集的提升机运行参数进行去噪预处理、多源信息融合，保证数据的准确性；然后将处理后的数据输入矿井提升机故障树，采用三角模糊数表示提升机底事件发生概率，得到底事件模糊概率；最后将模糊故障树映射为贝叶斯网络进行可靠性分析，将底事件模糊概率作为先验概率，计算叶节点发生概率，进而获得根节点的后验概率、概率重要度和关键重要度，从而快速确定故障类型和故障点。实例分析结果验证了该方法的可行性。Abstract: In order to solve problems of low efficiency and poor accuracy of existing mine hoist fault diagnosis methods, a fault diagnosis method of mine hoist based on fuzzy fault tree and Bayesian network was proposed. Firstly, denoising preprocessing and multi-source information fusion are carried out for hoist running parameters collected by sensors in real time, which can ensure accuracy of the data. Then the processed data is input into fault tree of mine hoist, and triangular fuzzy number is used to represent occurrence probability of bottom even to obtain fuzzy probability of bottom event. Finally, the fuzzy fault tree is mapped to Bayesian network for reliability analysis, and the fuzzy probability of bottom event is taken as priori probability to calculate probability of leaf node occurrence, thus posterior probability, probability importance and key importance of root node are obtained, so as to quickly determine fault type and fault location. The example analysis results verify feasibility of the method.
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Keywords:
- mine hoist /
- fault diagnosis /
- fuzzy fault tree /
- Bayesian network /
- reliability analysis
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期刊类型引用(3)
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