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. 種銀保. 臨床工程師規范化培訓教程. 北京: 科學出版社, 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. 周東華, 胡艷艷. 動態系統的故障診斷技術. 自動化學報, 2009, 35(6): 748-758.
5. 潘海洋, 鄭近德, 楊宇, 等. 基于 CELCD 和 MFVPMCD 的智能故障診斷方法研究. 電子學報, 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. 陳偉宏, 安吉堯, 李仁發, 等. 深度學習認知計算綜述. 自動化學報, 2017, 43(11): 1886-1897.
8. Schmidhuber J. Deep learning in neural networks. Amsterdam: Elsevier Science Ltd, 2015.
9. 周念成, 廖建權, 王強鋼, 等. 深度學習在智能電網中的應用現狀分析與展望. 電力系統自動化, 2019, 43(4): 180-191.
10. 上官偉, 孟月月, 楊嘉明, 等. 基于 LSTM-BP 級聯網絡的列控車載設備故障診斷. 北京交通大學學報, 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. 王維鋒, 邱雪歡, 孫劍橋, 等. 基于雙層長短時記憶網絡的齒輪故障診斷方法. 裝甲兵工程學院學報, 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. 劉香君, 種銀保, 肖晶晶, 等. 基于數據驅動的設備電路板無圖紙故障診斷. 中國醫學物理學雜志, 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. 大數據環境下基于信息論的入侵檢測數據歸一化方法. 武漢大學學報: 理學版, 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. 楊麗, 吳雨茜, 王俊麗, 等. 循環神經網絡研究綜述. 計算機應用, 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. 葉靖雯, 吳曉峰. 端到端深度圖像分割網絡中抑制無效率學習的目標損失函數設計. 微電子學與計算機, 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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