[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):1047-1052.[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(8):1047-1052.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.021]
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基于数据驱动的设备电路板无图纸故障诊断()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第8期
页码:
1047-1052
栏目:
医学信号处理与医学仪器
出版日期:
2020-08-27

文章信息/Info

Title:
Fault diagnosis for equipment circuit board based on data drive and no circuit drawing
文章编号:
1005-202X（2020）08-1047-06
作者:
刘香君; 种银保; 肖晶晶; 赵鹏; 张诗慧
陆军军医大学第二附属医院医学工程科， 重庆 400037
Author(s):
LIU Xiangjun;  CHONG Yinbao;  XIAO Jingjing;  ZHAO Peng;  ZHANG Shihui
Department of Medical Engineering, the Second Affiliated Hospital of Army Medical University, Chongqing 400037, China
关键词:
医疗设备; 电路板; 故障诊断; 长短时记忆网络
Keywords:
Keywords: medical equipment circuit board fault diagnosis long short-term memory
分类号:
R318.6
DOI:
DOI:10.3969/j.issn.1005-202X.2020.08.021
文献标志码:
A
摘要:
【摘要】针对医疗设备电路板结构设计复杂，传统故障诊断方法过度依赖图纸等技术资料和维修专家个人技术经验，导致维修贵、维修难等问题，提出一种基于数据驱动的无图纸故障智能诊断方法。在未知电路图纸信息以及电路板工作原理的前提下，模拟电路板不同故障状态，采集各外部接口引脚电信号作为原始故障数据；对故障数据进行预处理，并划分为训练集及测试集；使用机器学习的方法构建基于单层长短时记忆网络的故障智能诊断系统，利用Python编程进行模型训练，系统输出训练过程准确率及损失曲线。结果表明，该方法能实现对电路板故障的诊断分类，准确率达89.99%，效率较高，可靠性强。
Abstract:
Abstract: Considering the complicated design of medical equipment and the problems of traditional fault diagnosis methods such as the high cost and difficulties of maintenance due to the over-reliance on technical data such as circuit drawings, as well as the personal technical experience of maintenance specialists, an intelligent fault diagnosis method based on data drive and no circuit drawing is proposed. Under the premise of unknown circuit drawing information and the working principle of the circuit board, different fault states of the circuit board were simulated, and the electrical signals of each external interface pin were collected as the original fault data which were then preprocessed and divided into training set and test set. Machine learning method was used to construct an intelligent fault diagnosis model based on a single-layer long short-term memory network. The model training was carried out by Python programming, and the accuracy curve and loss curve of training progress were output. The result showed that the proposed method realized the diagnosis and classification of circuit board faults with high efficiency and strong reliability, and the accuracy reached 89.99%.

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[1]缪吉昌,纪晓宏,王兆源,等.基于威布尔分布的婴儿培养箱关键部件的剩余寿命预测[J].中国医学物理学杂志,2021,38(9):1162.[doi:10.3969/j.issn.1005-202X.2021.09.021]
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备注/Memo

备注/Memo:
【收稿日期】2020-02-20 【基金项目】国家重点研发计划项目（2016YFC0103100）；军队卫勤专项资助项目（20WQ005） 【作者简介】刘香君，硕士研究生，研究方向：医疗设备故障智能诊断，E-mail: xiangjunl6@163.com 【通信作者】种银保，教授，研究方向：医疗设备故障诊断与系统维护，E-mail: chongyinbao@163.net
更新日期/Last Update: 2020-08-27