[1]范莉萍,种银保,郎朗,等.基于聚合模糊数的多参数监护仪故障树研究[J].中国医学物理学杂志,2021,38(6):725-731.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.013]
　FAN Liping,CHONG Yinbao,LANG Lang,et al.Fault tree analysis for multi-parameter monitor based on aggregate fuzzy number[J].Chinese Journal of Medical Physics,2021,38(6):725-731.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.013]
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基于聚合模糊数的多参数监护仪故障树研究()
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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6期
页码:
725-731
栏目:
医学信号处理与医学仪器
出版日期:
2021-06-29

文章信息/Info

Title:
Fault tree analysis for multi-parameter monitor based on aggregate fuzzy number
文章编号:
1005-202X（2021）06-0725-07
作者:
范莉萍1; 种银保1; 郎朗1; 马建川1; 肖晶晶1; 刘香君2; 吕思敏1
1.陆军军医大学第二附属医院医学工程科， 重庆 400037； 2.中国人民解放军第32572部队， 贵州 安顺 561000
Author(s):
FAN Liping1;  CHONG Yinbao1;  LANG Lang1;  MA Jianchuan1;  XIAO Jingjing1;  LIU Xiangjun2;  L?Simin1
1.Department of Medical Engineering, the Second Affiliate Hospital of Army Medical University, Chongqing 400037, China 2. Unit 32572 of the Chinese Peoples Liberation Army, Anshun, 561000, China
关键词:
多参数监护仪; 故障诊断; 故障树; 聚合模糊数; Bland-Altman分析
Keywords:
Keywords: multi-parameter monitor fault diagnosis fault tree aggregate fuzzy number Bland-Altman analysis
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2021.06.013
文献标志码:
A
摘要:
针对多参数监护仪急救医疗设备故障率高，故障现象与故障原因对应复杂，且设备技术图纸缺乏、维修力量薄弱、厂家或第三方维修成本高等因素导致的维修困境，本文提出了基于聚合模糊数的多参数监护仪故障树故障诊断模型。首先，通过分析多参数监护仪的结构组成，建立故障树模型；其次，针对其故障数据缺乏和专家评价主观性的特点，采用聚合模糊数确定底事件故障率，并进行了底事件关键重要度分析；最后，采用Bland-Altman分析确定了本实验研究结果与验证实验结果一致性达到96.88%，证明了本文方法的有效性。本文的研究方法通过结合专家评价法和聚合模糊数弥补了故障数据缺乏和专家评价主观性的不足，适用于过程诊断中故障确定及事前潜在风险识别，为系统可靠性分析及故障诊断提供了思路。
Abstract:
Abstract: In view of the high failure rates of emergency medical equipments such as multi-parameter monitor and its maintenance dilemmas, including complex failure phenomena and failure causes, lack of equipment technical drawings, weak maintenance capabilities, high maintenance costs for manufacturers or third parties, etc., a fault diagnosis model for the fault tree analysis for multi-parameter monitor based on aggregate fuzzy number is proposed in the study. The fault tree model was firstly established by analyzing the structure of multi-parameter monitor and then considering its lack of fault data and the subjectivity of expert evaluation, aggregate fuzzy number was adopted to determine the failure rate of bottom events, and the critical importance of bottom events was analyzed. Finally, Bland-Altman analysis was used to confirm that the results of this experiment were 96.88% consistent with the results of verification experiments, which proved the effectiveness of the proposed method. The proposed method which makes up for the lack of fault data and the subjectivity of expert evaluation by combining expert evaluation method and aggregate fuzzy number is suitable for fault determination of process diagnosis and prior identification of potential risks, and it also provides an idea for system reliability analysis and fault diagnosis.

相似文献/References:

[1]刘香君,种银保,肖晶晶,等.基于数据驱动的设备电路板无图纸故障诊断[J].中国医学物理学杂志,2020,37(8):1047.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.021]
　LIU Xiangjun,CHONG Yinbao,XIAO Jingjing,et al.Fault diagnosis for equipment circuit board based on data drive and no circuit drawing[J].Chinese Journal of Medical Physics,2020,37(6):1047.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.021]
[2]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.[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(6):1129.[doi:10.3969/j.issn.1005-202X.2021.09.015]

备注/Memo

备注/Memo:
【收稿日期】2021-03-25 【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】范莉萍，硕士研究生，研究方向：医疗设备故障智能诊断，E-mail: lipingfan92@163.com 【通信作者】种银保，教授，研究方向：医疗设备故障智能诊断，E-mail: chongyinbao@163.net
更新日期/Last Update: 2021-06-29