Intelligent fault diagnosis of medical equipment based on long short term memory network_Journal of Biomedical Engineering

Authors：

LIU Xiangjun ^1,2 , LANG Lang ¹ , ZHANG Shihui ¹ , XIAO Jingjing ¹ , FAN Liping ¹ , MA Jianchuan ¹ ,  CHONG Yinbao ¹

1. Department of medical engineering, The Second Affiliate Hospital of Army Medical University, Chongqing 400037, P.R.China;
2. Unit 32572 of the Chinese People's Liberation Army, Anshun, Guizhou 561000, P.R.China;

Corresponding author：

CHONG Yinbao, Email: chongyinbao@163.net

Keywords：

long short term memory network; fault diagnosis; no circuit drawing; feature fusion; feature screening

DOI：

10.7507/1001-5515.201912019

Video：

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In order to solve the current problems in medical equipment maintenance, this study proposed an intelligent fault diagnosis method for medical equipment based on long short term memory network(LSTM). Firstly, in the case of no circuit drawings and unknown circuit board signal direction, the symptom phenomenon and port electrical signal of 7 different fault categories were collected, and the feature coding, normalization, fusion and screening were preprocessed. Then, the intelligent fault diagnosis model was built based on LSTM, and the fused and screened multi-modal features were used to carry out the fault diagnosis classification and identification experiment. The results were compared with those using port electrical signal, symptom phenomenon and the fusion of the two types. In addition, the fault diagnosis algorithm was compared with BP neural network (BPNN), recurrent neural network (RNN) and convolution neural network (CNN). The results show that based on the fused and screened multi-modal features, the average classification accuracy of LSTM algorithm model reaches 0.970 9, which is higher than that of using port electrical signal alone, symptom phenomenon alone or the fusion of the two types. It also has higher accuracy than BPNN, RNN and CNN, which provides a relatively feasible new idea for intelligent fault diagnosis of similar equipment.

Citation： LIU Xiangjun, LANG Lang, ZHANG Shihui, XIAO Jingjing, FAN Liping, MA Jianchuan, CHONG Yinbao. Intelligent fault diagnosis of medical equipment based on long short term memory network. Journal of Biomedical Engineering, 2021, 38(2): 361-368. doi: 10.7507/1001-5515.201912019 Copy

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1. Perry L, Malkin R. Effectiveness of medical equipment donations to improve health systems: how much medical equipment is broken in the developing world?. Med Biol Eng Comput, 2011, 49(7): 719-722.
2. 种银保. 临床工程师规范化培训教程. 北京: 科学出版社, 2017: 24-27.
3. Webber C M, Martínez-Gálvez G, Higuita M L, et al. Developing strategies for sustainable medical equipment maintenance in under-resourced settings. Ann Glob Health, 2020, 86(1): 39.
4. 周东华, 胡艳艳. 动态系统的故障诊断技术. 自动化学报, 2009, 35(6): 748-758.
5. 潘海洋, 郑近德, 杨宇, 等. 基于 CELCD 和 MFVPMCD 的智能故障诊断方法研究. 电子学报, 2017, 45(3): 546-551.
6. Deng L, Yu D. Deep learning: methods and applications. Foundations & Trends in Signal Processing, 2014, 7(3): 197-387.
7. 陈伟宏, 安吉尧, 李仁发, 等. 深度学习认知计算综述. 自动化学报, 2017, 43(11): 1886-1897.
8. Schmidhuber J. Deep learning in neural networks. Amsterdam: Elsevier Science Ltd, 2015.
9. 周念成, 廖建权, 王强钢, 等. 深度学习在智能电网中的应用现状分析与展望. 电力系统自动化, 2019, 43(4): 180-191.
10. 上官伟, 孟月月, 杨嘉明, 等. 基于 LSTM-BP 级联网络的列控车载设备故障诊断. 北京交通大学学报, 2019, 43(1): 54-62.
11. Huang Y Q. The research on aero-engine gas path fault diagnosis by genetic algorithm-BP neural network. Machine Tool & Hydraulics, 2015, 43(18): 31-36.
12. Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93(7): 490-502.
13. Felix A G, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Comput, 2000, 12(10): 2451-2471.
14. Wang Weifeng, Qiu Xuehuan, Chen Caisen, et al. Application research on long short-term memory network in fault diagnosis//2018 International Conference on Machine Learning and Cybernetics (ICMLC), Chengdu: IEEE, 2018: 360-365.
15. 王维锋, 邱雪欢, 孙剑桥, 等. 基于双层长短时记忆网络的齿轮故障诊断方法. 装甲兵工程学院学报, 2018, 32(2): 81-85.
16. de Bruin T, Verbert K, Babuska R. Railway track circuit fault diagnosis using recurrent neural networks. IEEE Trans Neural Netw Learn Syst, 2017, 28(3): 523-533.
17. 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.
18. Ma Yixuan, Zhang Zhenji. Travel mode choice prediction using deep neural networks with entity embeddings. IEEE Access, 2020, 8: 64959-64970.
19. 宋勇, 蔡志平. 大数据环境下基于信息论的入侵检测数据归一化方法. 武汉大学学报: 理学版, 2018, 64(2): 121-126.
20. Wang Xi, Li Qiang, Xie Zhihong. New Feature Selection Method Based on SVM-RFE. Advanced Materials Research, 2014, 926-930: 3100-3104.
21. Graves A. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer, 2012.
22. 杨丽, 吴雨茜, 王俊丽, 等. 循环神经网络研究综述. 计算机应用, 2018, 38(s2): 1-6, 26.
23. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, 2012, 1: 1097-1105.
24. Torfi A, Iranmanesh S M, Nasrabadi N, et al. 3D convolutional neural networks for cross audio-visual matching recognition. IEEE Access, 2017, 5: 22081-22091.
25. Li W, Meng Y. Improving the performance of neural networks with random forest in detecting network intrusions//The 10th International Symposium on Neural Networks (ISNN), 2013, 7952: 622-629.
26. 叶靖雯, 吴晓峰. 端到端深度图像分割网络中抑制无效率学习的目标损失函数设计. 微电子学与计算机, 2019, 36(9): 38-43.
27. Hasnain M, Pasha M F, Ghani I, et al. Evaluating trust prediction and confusion matrix measures for web services ranking. IEEE Access, 2020, 8: 90847-90861.
28. Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks//30th International Conference on Machine Learning (ICML'13), Atlanta: International Machine Learning Society (IMLS), 2013, 28(3): 1310-1318.
29. Yao Yuan, Rosasco L, Caponnetto A. On early stopping in gradient descent learning. Constr Approx, 2007, 26(2): 289-315.

Journal of Biomedical Engineering

Intelligent fault diagnosis of medical equipment based on long short term memory network

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