Research on fault diagnosis of patient monitor based on text mining_Journal of Biomedical Engineering

Authors：

HE Xiangfei ¹ ,  ZHANG Hehua ^2,3 , HUANG Jing ² , ZHAO Dechun ³ , LI Yang ² , NIE Rui ² , LIU Xianghua ²

1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;
2. Department of Medical Engineering, Daping Hospital of Army Medical University, Chongqing 400042, P. R. China;
3. School of Biological Information, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China;

Corresponding?author：

ZHANG Hehua, Email: zhanghehua@vip.163.com

Keywords：

Patient monitor; Fault diagnosis; Text mining; Pre-trained language models; Attention mechanism

DOI：

10.7507/1001-5515.202306017

Video：

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Abstract Full text Figures/Tables Video References Cited by

The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer. Then, the bidirectional fault features were extracted and weighted by the improved BiGRU and attention mechanism respectively. Finally, the weighted loss function is used to reduce the impact of class imbalance on the model. To validate the effectiveness of the proposed method, this paper uses the patient monitor fault dataset for verification, and the macro F1 value has achieved 91.11%. The results show that the model built in this study can realize the automatic classification of fault text, and may provide assistant decision support for the intelligent fault diagnosis of the patient monitor in the future.

Citation： HE Xiangfei, ZHANG Hehua, HUANG Jing, ZHAO Dechun, LI Yang, NIE Rui, LIU Xianghua. Research on fault diagnosis of patient monitor based on text mining. Journal of Biomedical Engineering, 2024, 41(1): 168-176. doi: 10.7507/1001-5515.202306017 Copy

1.	范莉萍, 郎朗, 肖晶晶, 等. 基于故障樹的多參數監護儀故障智能診斷專家系統研究. 生物醫學工程學雜志, 2022, 39(3): 586-595.
2.	Chen Mengqi, Qu Rong, Fang Weiguo. Case-based reasoning system for fault diagnosis of aero-engines. Expert Systems with Applications, 2022, 202: 117350.
3.	趙京勝, 宋夢雪, 高祥, 等. 自然語言處理中的文本表示研究. 軟件學報, 2022, 33(1): 102-128.
4.	Wang Z, Wang J, Chen S. Fault location of strip steel surface quality defects on hot-rolling production line based on information fusion of historical cases and process data. IEEE Access, 2020, 8: 171240-171251.
5.	侯通, 鄭啟明, 姚新文, 等. 基于文本挖掘的軌道電路細粒度故障致因分析方法. 鐵道學報, 2022, 44(10): 73-81.
6.	Yu D, Xiang B. Discovering topics and trends in the field of artificial intelligence: using LDA topic modeling. Expert Systems with Applications, 2023, 225: 120114.
7.	Gupta R K, Agarwalla R, Naik B H, et al. Prediction of research trends using LDA based topic modeling. Global Transitions Proceedings, 2022, 3(1): 298-304.
8.	Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space.(2013-09-07)[2023-5-23]. https: //arxiv.org/abs/1301.3781.
9.	Xu Z, Chen B, Zhou S, et al. A text-driven aircraft fault diagnosis model based on a Word2vec and priori-knowledge convolutional neural network. Aerospace, 2021, 8(4): 1-15.
10.	林海香, 趙正祥, 陸人杰, 等. 基于字詞融合的高鐵道岔多級故障診斷組合模型. 電子測量與儀器學報, 2022, 36(10): 217-226.
11.	Huan H, Yan J, Xie Y, et al. Feature-enhanced nonequilibrium bidirectional long short-term memory model for Chinese text classification. IEEE Access, 2020, 8: 199629-199637.
12.	賈寶惠, 姜番, 王玉鑫, 等. 基于民機維修文本數據的故障診斷方法. 航空學報, 2023, 44(5): 258-272.
13.	郭崇慧, 徐良辰, 魏偉, 等. 基于文本挖掘的新型冠狀病毒肺炎診療方案演化分析. 生物醫學工程學雜志, 2021, 38(2): 197-209.
14.	Qiu X, Sun T, Xu Y, et al. Pre-trained models for natural language processing: a survey. Science China Technological Sciences, 2020, 63: 1872-1897.
15.	Jawahar G, Sagot B, Seddah D. What does BERT learn about the structure of language? // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence: Association for Computational Linguistics, 2020: 3651–3657.
16.	Akhter M P, Zheng Jiangbin, Naqvi I R, et al. Document-level text classification using single-layer multisize filters convolutional neural network. IEEE Access, 2020, 8: 42689-42707.
17.	蒲曉蓉, 陳柯成, 劉軍池, 等. COVID-19 中國診療方案的機器學習智能分析方法. 生物醫學工程學雜志, 2020, 37(3): 365-372.
18.	沙爾旦爾·帕爾哈提, 米吉提·阿不里米提, 艾斯卡爾·艾木都拉. 基于穩健詞素序列和LSTM的維吾爾語短文本分類. 中文信息學報, 2020, 34(1): 63-70.
19.	趙宏, 傅兆陽, 王樂. 基于特征融合的中文文本情感分析方法. 蘭州理工大學學報, 2022, 48(3): 94-102.
20.	方正云, 楊政, 李麗敏, 等. 基于交叉注意力機制的多視圖項目文本分類方法. 中文信息學報, 2022, 36(7): 123-131.
21.	劉鵬程, 孫林夫, 張常有, 等. 基于交互注意力機制網絡模型的故障文本分類. 計算機集成制造系統, 2021, 27(1): 72-89.
22.	Li Y, Zhang S, Lai C. Agricultural text classification method based on dynamic fusion of multiple features. IEEE Access, 2023, 11: 27034-27042.
23.	Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate.(2016-05-19)[2023-5-23]. arXiv:1409.0473.
24.	陳可嘉, 劉惠. 基于改進BiGRU-CNN的中文文本分類方法. 計算機工程, 2022, 48(5): 59-66,73.
25.	吳庭偉, 王夢靈, 易樹平, 等. 多尺度核電質量文本故障信息語義抽取方法. 中國機械工程, 2023, 34(8): 976-981,992.
26.	Xu L, Hu H, Zhang X W, et al. CLUE: a Chinese language understanding evaluation benchmark//Proceedings of the 28th International Conference on Computational Linguistics. Barcelona: International Committee on Computational Linguistics, 2020: 4762–4772.
27.	Cui Yiming, Che Wanxiang, Wang Shijin, et al. LERT: a linguistically-motivated pre-trained language model.(2022-11-10)[2023-5-23]. arXiv:2211.05344.
28.	Shi L, Li A, Zhang L. Sustainable fault diagnosis of imbalanced text mining for CTCS-3 data preprocessing. Sustainability, 2021, 13(4): 1-14.
29.	周慶華, 李曉麗. 基于MCNN的鐵路信號設備故障短文本分類方法研究. 鐵道科學與工程學報, 2019, 16(11): 2859-2865.
30.	鄧維斌, 朱坤, 李云波, 等. FMNN: 融合多神經網絡的文本分類模型. 計算機科學, 2022, 49(3): 281-287.

1. 范莉萍, 郎朗, 肖晶晶, 等. 基于故障樹的多參數監護儀故障智能診斷專家系統研究. 生物醫學工程學雜志, 2022, 39(3): 586-595.
2. Chen Mengqi, Qu Rong, Fang Weiguo. Case-based reasoning system for fault diagnosis of aero-engines. Expert Systems with Applications, 2022, 202: 117350.
3. 趙京勝, 宋夢雪, 高祥, 等. 自然語言處理中的文本表示研究. 軟件學報, 2022, 33(1): 102-128.
4. Wang Z, Wang J, Chen S. Fault location of strip steel surface quality defects on hot-rolling production line based on information fusion of historical cases and process data. IEEE Access, 2020, 8: 171240-171251.
5. 侯通, 鄭啟明, 姚新文, 等. 基于文本挖掘的軌道電路細粒度故障致因分析方法. 鐵道學報, 2022, 44(10): 73-81.
6. Yu D, Xiang B. Discovering topics and trends in the field of artificial intelligence: using LDA topic modeling. Expert Systems with Applications, 2023, 225: 120114.
7. Gupta R K, Agarwalla R, Naik B H, et al. Prediction of research trends using LDA based topic modeling. Global Transitions Proceedings, 2022, 3(1): 298-304.
8. Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space.(2013-09-07)[2023-5-23]. https: //arxiv.org/abs/1301.3781.
9. Xu Z, Chen B, Zhou S, et al. A text-driven aircraft fault diagnosis model based on a Word2vec and priori-knowledge convolutional neural network. Aerospace, 2021, 8(4): 1-15.
10. 林海香, 趙正祥, 陸人杰, 等. 基于字詞融合的高鐵道岔多級故障診斷組合模型. 電子測量與儀器學報, 2022, 36(10): 217-226.
11. Huan H, Yan J, Xie Y, et al. Feature-enhanced nonequilibrium bidirectional long short-term memory model for Chinese text classification. IEEE Access, 2020, 8: 199629-199637.
12. 賈寶惠, 姜番, 王玉鑫, 等. 基于民機維修文本數據的故障診斷方法. 航空學報, 2023, 44(5): 258-272.
13. 郭崇慧, 徐良辰, 魏偉, 等. 基于文本挖掘的新型冠狀病毒肺炎診療方案演化分析. 生物醫學工程學雜志, 2021, 38(2): 197-209.
14. Qiu X, Sun T, Xu Y, et al. Pre-trained models for natural language processing: a survey. Science China Technological Sciences, 2020, 63: 1872-1897.
15. Jawahar G, Sagot B, Seddah D. What does BERT learn about the structure of language? // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence: Association for Computational Linguistics, 2020: 3651–3657.
16. Akhter M P, Zheng Jiangbin, Naqvi I R, et al. Document-level text classification using single-layer multisize filters convolutional neural network. IEEE Access, 2020, 8: 42689-42707.
17. 蒲曉蓉, 陳柯成, 劉軍池, 等. COVID-19 中國診療方案的機器學習智能分析方法. 生物醫學工程學雜志, 2020, 37(3): 365-372.
18. 沙爾旦爾·帕爾哈提, 米吉提·阿不里米提, 艾斯卡爾·艾木都拉. 基于穩健詞素序列和LSTM的維吾爾語短文本分類. 中文信息學報, 2020, 34(1): 63-70.
19. 趙宏, 傅兆陽, 王樂. 基于特征融合的中文文本情感分析方法. 蘭州理工大學學報, 2022, 48(3): 94-102.
20. 方正云, 楊政, 李麗敏, 等. 基于交叉注意力機制的多視圖項目文本分類方法. 中文信息學報, 2022, 36(7): 123-131.
21. 劉鵬程, 孫林夫, 張常有, 等. 基于交互注意力機制網絡模型的故障文本分類. 計算機集成制造系統, 2021, 27(1): 72-89.
22. Li Y, Zhang S, Lai C. Agricultural text classification method based on dynamic fusion of multiple features. IEEE Access, 2023, 11: 27034-27042.
23. Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate.(2016-05-19)[2023-5-23]. arXiv:1409.0473.
24. 陳可嘉, 劉惠. 基于改進BiGRU-CNN的中文文本分類方法. 計算機工程, 2022, 48(5): 59-66,73.
25. 吳庭偉, 王夢靈, 易樹平, 等. 多尺度核電質量文本故障信息語義抽取方法. 中國機械工程, 2023, 34(8): 976-981,992.
26. Xu L, Hu H, Zhang X W, et al. CLUE: a Chinese language understanding evaluation benchmark//Proceedings of the 28th International Conference on Computational Linguistics. Barcelona: International Committee on Computational Linguistics, 2020: 4762–4772.
27. Cui Yiming, Che Wanxiang, Wang Shijin, et al. LERT: a linguistically-motivated pre-trained language model.(2022-11-10)[2023-5-23]. arXiv:2211.05344.
28. Shi L, Li A, Zhang L. Sustainable fault diagnosis of imbalanced text mining for CTCS-3 data preprocessing. Sustainability, 2021, 13(4): 1-14.
29. 周慶華, 李曉麗. 基于MCNN的鐵路信號設備故障短文本分類方法研究. 鐵道科學與工程學報, 2019, 16(11): 2859-2865.
30. 鄧維斌, 朱坤, 李云波, 等. FMNN: 融合多神經網絡的文本分類模型. 計算機科學, 2022, 49(3): 281-287.

Journal of Biomedical Engineering

Research on fault diagnosis of patient monitor based on text mining

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