基于故障树与贝叶斯网络的呼吸机故障智能诊断

为了快速准确地找出呼吸机故障原因，迅速排除故障，恢复设备的正常运行，本文采用基于故障树和贝叶斯网络的 方法对呼吸机常见故障进行分析。首先通过对呼吸机结构原理的综合分析，结合文献案例搭建呼吸机故障树，进行定性 分析；利用贝叶斯网络对呼吸机故障进行定量分析；最后用实际维修案例进行验证。结果表明，该方法得到的推理结果与 实际结果相符性达到84.54%，为建立呼吸机故障静态数据库并进行故障智能诊断提供了理论依据，具有一定的推广 价值。

Intelligent fault diagnosis of ventilator based on fault tree and Bayesian network

In order to find out the cause of the ventilator fault quickly and accurately, troubleshoot the fault and restore the normal operation of the equipment quickly, a method based on fault tree and Bayesian network is used for analyzing the common faults of the ventilator. Based on the comprehensive analysis on the structural principle of the ventilator, combined with literature cases, a fault tree of the ventilator is established for qualitative analysis and the ventilator faults are quantitatively analyzed by Bayesian network. Finally, the actual maintenance cases are used for validation. The results show that the reasoning results obtained by the proposed method are consistent with the actual results, with a consistency up to 84.54%, which provides a theoretical basis for the establishment of static database of ventilator faults and intelligent fault diagnosis, with certain value of promotion.