[1]马建川,种银保,郎朗,等.基于故障树与贝叶斯网络的呼吸机故障智能诊断[J].中国医学物理学杂志,2021,38(9):1129-1135.[doi:10.3969/j.issn.1005-202X.2021.09.015]
　MA Jianchuan,CHONG Yinbao,LANG Lang,et al.Intelligent fault diagnosis of ventilator based on fault tree and Bayesian network[J].Chinese Journal of Medical Physics,2021,38(9):1129-1135.[doi:10.3969/j.issn.1005-202X.2021.09.015]
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基于故障树与贝叶斯网络的呼吸机故障智能诊断()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第9期
页码:
1129-1135
栏目:
医学信号处理与医学仪器
出版日期:
2021-09-26

文章信息/Info

Title:
Intelligent fault diagnosis of ventilator based on fault tree and Bayesian network
文章编号:
1005-202X（2021）09-1129-07
作者:
马建川1; 种银保1; 郎朗1; 肖晶晶1; 王晴1; 范莉萍1; 刘香君2
1. 中国人民解放军陆军军医大学第二附属医院医学工程科，重庆400000；2. 中国人民解放军第32572 部队，贵州安顺561000
Author(s):
MA Jianchuan1;  CHONG Yinbao1;  LANG Lang1;  XIAO Jingjing1;  WANG Qing1;  FAN Liping1;  LIU Xiangjun2
1. Department of Medical Engineering, the Second Affiliated Hospital of Army Medical University, Chongqing 400000, China 2. Unit 32572 of the Chinese Peoples Liberation Army, Anshun 561000, China
关键词:
呼吸机; 故障树分析; 贝叶斯网络; 故障诊断
Keywords:
ventilator fault tree analysis Bayesian network fault analysis
分类号:
R318.6
DOI:
10.3969/j.issn.1005-202X.2021.09.015
文献标志码:
A
摘要:
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。
Abstract:
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.

相似文献/References:

[1]邝 勇,江贵平.基于磁场矢量控制的持续正压通气呼吸机设计[J].中国医学物理学杂志,2015,32(04):493.[doi:10.3969/j.issn.1005-202X.2015.04.009]
　[J].Chinese Journal of Medical Physics,2015,32(9):493.[doi:10.3969/j.issn.1005-202X.2015.04.009]
[2]马雪,周世辉.呼吸机潮气量和高压报警值参数设置在心肺复苏中的应用[J].中国医学物理学杂志,2021,38(8):1001.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.016]
　MA Xue,ZHOU Shihui.Application of parameter settings of ventilator tidal volume and high pressure alarm value in cardiopulmonary resuscitation[J].Chinese Journal of Medical Physics,2021,38(9):1001.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.016]

备注/Memo

备注/Memo:
【收稿日期】2021-05-09 【基金项目】国家重点研发计划（2016YFC0103100）；军队卫勤专项资 助项目（20WQ005） 【作者简介】马建川，硕士，研究方向：医疗设备故障智能诊断，E-mail: 1253010852@qq.com 【通信作者】种银保，教授，研究方
更新日期/Last Update: 2021-09-27