Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree_Journal of Biomedical Engineering

Authors：

FAN Liping ^1,2 ,  LANG Lang ¹ , XIAO Jingjing ¹ , ZHANG Shihui ¹ , CHONG Yinbao ¹ , LYU Simin ¹

1. Department of Medical Engineering, The Second Affiliate Hospital of Army Medical University, Chongqing 400037, P. R. China;
2. Department of Medical Engineering, No. 922 Hospital of Joint Logistics Support Force, Hengyang, Hunan 421200, P. R. China;

Corresponding author：

LANG Lang, Email: 16530595@qq.com

Keywords：

Fault tree; Intelligent fault diagnosis; Expert system; Technology fusion

DOI：

10.7507/1001-5515.202110009

Video：

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Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.

Citation： FAN Liping, LANG Lang, XIAO Jingjing, ZHANG Shihui, CHONG Yinbao, LYU Simin. Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree. Journal of Biomedical Engineering, 2022, 39(3): 586-595. doi: 10.7507/1001-5515.202110009 Copy

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8.	王得水, 戴建荣, 蔡军, 等. 多参数心电监护仪故障的统计分析及维修方法. 生物医学工程与临床, 2018, 22(3): 333-337.
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10.	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. 中国医学物理学杂志, 2021, 38(6): 725-731.
11.	Wang J, Ding D, Liu O, et al. A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems. Applied Soft Computing, 2016, 39: 11-20.
12.	Kumar M, Kaushik M. System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment. J Loss Prev Process Ind, 2020, 67(2): 104236.
13.	兰蓉, 范九伦. 三角模糊数上的完备度量及其在决策中的应用. 系统工程学报, 2010, 25(3): 313-319.
14.	于文杰, 郭国平, 吴兵. 基于模糊故障树的长江LNG船舶装卸作业风险预测. 交通信息与安全, 2019, 37(5): 46-53.
15.	Wang F, Zheng P, Dai J, et al. Fault tree analysis of the causes of urban smog events associated with vehicle exhaust emissions: a case study in Jinan, China. Sci Total Environ, 2019, 668: 245-253.
16.	Saibene A, Assale M, Giltri M. Expert Systems: definitions, advantages and issues in medical field applications. Expert Systems with Applications, 2021, 177: 114900.
17.	Qian Y, Xu L, Li X X, et al. LUBRES: an expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process. Expert Syst Appl, 2008, 35(3): 1252-1266.
18.	Sarazin A, Bascans J, Sciau J B, et al. Expert system dedicated to condition-based maintenance based on a knowledge graph approach: application to an aeronautic system. Expert Systems with Applications, 2021, 186: 115767.
19.	杨一鸣, 汪贵平. 面向对象的无人车电源故障检测专家系统设计. 汽车技术, 2019(6): 30-35.
20.	袁杰, 王福利, 王姝, 等. 基于D-S融合的混合专家知识系统故障诊断方法. 自动化学报, 2017, 43(9): 1580-1587.
21.	官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展. 软件学报, 2018, 29(10): 2966-2994.
22.	Wang Yuan, Ji Wen, Wang Jianhui. Design and implementation of inference engine for conflict resolution//2012 Second International Conference on Intelligent System Design and Engineering Application, Sanya: Hunan University of Technology, 2012: 220-223.

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. 中国医学物理学杂志, 2020, 37(8): 1047-1052.
2. Israelski E W, Muto W H. Human factors risk management as a way to improve medical device safety: a case study of the therac 25 radiation therapy system. Jt Comm J Qual Saf, 2004, 30(12): 689-695.
3. 张诗慧, 种银保, 肖晶晶, 等. 基于LabVIEW的医疗设备故障智能诊断系统. 仪表技术与传感器, 2018(4): 98-103.
4. 刘香君, 郎朗, 张诗慧, 等. 基于长短时记忆网络的医疗设备故障智能诊断研究. 生物医学工程学杂志, 2021, 38(2): 361-368.
5. 陈庆. 多参数监护仪安全风险分析. 中国医学装备, 2016, 13(10): 29-32, 33.
6. Jafari M J, Pouyakian M, Khanteymoori A, et al. Reliability evaluation of fire alarm systems using dynamic bayesian networks and fuzzy fault tree analysis. J Loss Prev Process Ind, 2020, 67(7): 104229.
7. Kabir S. An overview of fault tree analysis and its application in model based dependability analysis. Expert Systems with Applications, 2017, 77: 114-135.
8. 王得水, 戴建荣, 蔡军, 等. 多参数心电监护仪故障的统计分析及维修方法. 生物医学工程与临床, 2018, 22(3): 333-337.
9. Oliver M A, Guerrero L, Diaz I, et al. Determination of minimal cut sets of a complex fault tree. Comput Ind Eng, 1997, 33(1): 59-62.
10. 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. 中国医学物理学杂志, 2021, 38(6): 725-731.
11. Wang J, Ding D, Liu O, et al. A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems. Applied Soft Computing, 2016, 39: 11-20.
12. Kumar M, Kaushik M. System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment. J Loss Prev Process Ind, 2020, 67(2): 104236.
13. 兰蓉, 范九伦. 三角模糊数上的完备度量及其在决策中的应用. 系统工程学报, 2010, 25(3): 313-319.
14. 于文杰, 郭国平, 吴兵. 基于模糊故障树的长江LNG船舶装卸作业风险预测. 交通信息与安全, 2019, 37(5): 46-53.
15. Wang F, Zheng P, Dai J, et al. Fault tree analysis of the causes of urban smog events associated with vehicle exhaust emissions: a case study in Jinan, China. Sci Total Environ, 2019, 668: 245-253.
16. Saibene A, Assale M, Giltri M. Expert Systems: definitions, advantages and issues in medical field applications. Expert Systems with Applications, 2021, 177: 114900.
17. Qian Y, Xu L, Li X X, et al. LUBRES: an expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process. Expert Syst Appl, 2008, 35(3): 1252-1266.
18. Sarazin A, Bascans J, Sciau J B, et al. Expert system dedicated to condition-based maintenance based on a knowledge graph approach: application to an aeronautic system. Expert Systems with Applications, 2021, 186: 115767.
19. 杨一鸣, 汪贵平. 面向对象的无人车电源故障检测专家系统设计. 汽车技术, 2019(6): 30-35.
20. 袁杰, 王福利, 王姝, 等. 基于D-S融合的混合专家知识系统故障诊断方法. 自动化学报, 2017, 43(9): 1580-1587.
21. 官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展. 软件学报, 2018, 29(10): 2966-2994.
22. Wang Yuan, Ji Wen, Wang Jianhui. Design and implementation of inference engine for conflict resolution//2012 Second International Conference on Intelligent System Design and Engineering Application, Sanya: Hunan University of Technology, 2012: 220-223.

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