Named entity recognition based on equipment and fault field of CNC machine tools

WANG Huan; ZHU Wen-qiu; WU Yue-zhong; HE Pin-jie; WAN Lan-jun

doi:10.13374/j.issn2095-9389.2019.09.17.002

Volume 42 Issue 4

Apr. 2020

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Article Navigation > Chinese Journal of Engineering > 2020 > 42(4): 476-482

WANG Huan, ZHU Wen-qiu, WU Yue-zhong, HE Pin-jie, WAN Lan-jun. Named entity recognition based on equipment and fault field of CNC machine tools[J]. Chinese Journal of Engineering, 2020, 42(4): 476-482. doi: 10.13374/j.issn2095-9389.2019.09.17.002

Citation:

WANG Huan, ZHU Wen-qiu, WU Yue-zhong, HE Pin-jie, WAN Lan-jun. Named entity recognition based on equipment and fault field of CNC machine tools[J]. Chinese Journal of Engineering, 2020, 42(4): 476-482. doi: 10.13374/j.issn2095-9389.2019.09.17.002

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Named entity recognition based on equipment and fault field of CNC machine tools

doi: 10.13374/j.issn2095-9389.2019.09.17.002

WANG Huan^{1, 2},
ZHU Wen-qiu^{1, 2},
WU Yue-zhong^{1, 2
,
,},
HE Pin-jie^{1, 2},
WAN Lan-jun^{1, 2}

1.
School of Computer, Hunan University of Technology, Zhuzhou 412008, China
2.
Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Zhuzhou 412008, China

More Information

Corresponding author: E-mail: yuezhong.wu@163.com
Received Date: 2019-09-17
Publish Date: 2020-04-01

Abstract

Abstract

With the advent of intelligent manufacturing and big data, the Made in China 2025 Initiative and Industry 4.0 have been paying increasing attention to automation and intelligent industrial equipment. In the background of the present times, the complexity and intelligence of computer numerical control (CNC) machine tools have been continuously improved, and the types and descriptions of CNC machine tools’ faults have increased, presenting serious challenges to equipment maintenance and diagnosis of CNC machine tools. In order to provide guarantee for accurate fault diagnosis of CNC machine tools, and to prolong the service life of CNC machine tools, it is necessary to improve the performance of named entity recognition system. Accordingly, the named entity recognition in the equipment and faults field of CNC machine tools were studied, taking the historical examinations and repair records of CNC machine tools as the research object. After analyzing the characteristics of fault description in the historical examinations and repair records, a named entity recognition method was proposed based on the combination of bidirectional long short-term memory (BLSTM) and conditional random field with loop (L-CRF). The first step is to input a sentence and segment and label the input sentence. The annotation corpus is combined with the pre-trained generated word vector by using Skip-gram model in Word2vec, and the word vector is converted into a word vector sequence through the word embedding layer. In the second step, the word vector sequence is integrated into the BLSTM layer to learn long term dependency information. The final step is to input the sentence expression into the L-CRF layer to obtain the global optimal sequence. The experimental results show that the method is superior to other named entity recognition methods, which lays a solid foundation for the intelligent maintenance and the real-time diagnostic tasks of CNC machine tools.

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References(25)

References

[1]	曾建電, 王田, 賈維嘉, 等. 傳感云研究綜述. 計算機研究與發展, 2017, 54(5):925 doi: 10.7544/issn1000-1239.2017.20160492 Zeng J D, Wang T, Jia W J, et al. A survey on sensor-cloud. J Comput Res Dev, 2017, 54(5): 925 doi: 10.7544/issn1000-1239.2017.20160492
[2]	王田, 沈雪微, 羅皓, 等. 基于霧計算的可信傳感云研究進展. 通信學報, 2019, 40(3):170 doi: 10.11959/j.issn.1000-436x.2019068 Wang T, Shen X W, Luo H, et al. Research progress of trusted sensor-cloud based on fog computing. J Commun, 2019, 40(3): 170 doi: 10.11959/j.issn.1000-436x.2019068
[3]	李江昀, 趙義凱, 薛卓爾, 等. 深度神經網絡模型壓縮綜述. 工程科學學報, 2019, 41(10):1229 Li J Y, Zhao Y K, Xue Z E, et al. A survey of model compression for deep neural networks. Chin J Eng, 2019, 41(10): 1229
[4]	Bikel D M, Miller S, Schwartz R, et al. Nymble: a high-performance learning name-finder[J/OL]. arXiv preprint (1998-03-27)[2019-09-17]. https://arxiv.org/abs/cmp-lg/9803003
[5]	Berger A L, Pietra V J D, Pietra S A D. A maximum entropy approach to natural language processing. Computat Linguist, 1996, 22(1): 39
[6]	McCallum A, Li W. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons // Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003-Volume 4. Edmonton, 2003: 188
[7]	俞鴻魁, 張華平, 劉群, 等. 基于層疊隱馬爾可夫模型的中文命名實體識別. 通信學報, 2006, 27(2):87 doi: 10.3321/j.issn:1000-436X.2006.02.013 Yu H K, Zhang H P, Liu Q, et al. Chinese named entity recognition based on cascading hidden Markov model. J Commun, 2006, 27(2): 87 doi: 10.3321/j.issn:1000-436X.2006.02.013
[8]	何炎祥, 羅楚威, 胡彬堯. 基于CRF和規則相結合的地理命名實體識別方法. 計算機應用與軟件, 2015, 32(1):179 doi: 10.3969/j.issn.1000-386x.2015.01.046 He Y X, Luo C W, Hu B Y. Geographic entity recognition method based on CRF model and rules combination. Comput Appl Softw, 2015, 32(1): 179 doi: 10.3969/j.issn.1000-386x.2015.01.046
[9]	王路路, 艾山?吾買爾, 買合木提?買買提, 等. 基于CRF和半監督學習的維吾爾文命名實體識別. 中文信息學報, 2018, 32(11):16 doi: 10.3969/j.issn.1003-0077.2018.11.003 Wang L L, Aishan W, Maihemuti M, et al. A semi-supervised approach to Uyghur named entity recognition based on CRF. J Chin Inf Process, 2018, 32(11): 16 doi: 10.3969/j.issn.1003-0077.2018.11.003
[10]	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735 doi: 10.1162/neco.1997.9.8.1735
[11]	Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw, 2005, 18(5-6): 602 doi: 10.1016/j.neunet.2005.06.042
[12]	楊紅梅, 李琳, 楊日東, 等. 基于雙向LSTM神經網絡電子病歷命名實體的識別模型. 中國組織工程研究, 2018, 22(20):3237 doi: 10.3969/j.issn.2095-4344.0302 Yang H M, Li L, Yang R D, et al. Named entity recognition based on bidirectional long short-term memory combined with case report form. Chin J Tissue Eng Res, 2018, 22(20): 3237 doi: 10.3969/j.issn.2095-4344.0302
[13]	Lin B Y, Xu F, Luo Z Y, et al. Multi-channel BiLSTM-CRF model for emerging named entity recognition in social media // Proceedings of the 3rd Workshop on Noisy User-generated Text. Copenhagen, 2017: 160
[14]	Bharadwaj A, Mortensen D, Dyer C, et al. Phonologically aware neural model for named entity recognition in low resource transfer settings // Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, 2016: 1462
[15]	Li C Y, Wu Y Z, Hu F H, et al. Packaging domain-based named entity recognition with multi-layer neural networks. Neu-roQuantology, 2018, 16(6): 564
[16]	易士翔, 尹宏鵬, 鄭恒毅. 基于BiLSTM的公共安全事件觸發詞識別. 工程科學學報, 2019, 41(9):1201 Yi S X, Yin H P, Zheng H Y. Public security event trigger identification based on Bidirectional LSTM. Chin J Eng, 2019, 41(9): 1201
[17]	陳秋瑗, 程光, 李迪, 等. 機械設計領域的命名實體識別研究. 計算機工程與應用, 2017, 53(20):100 doi: 10.3778/j.issn.1002-8331.1604-0231 Chen Q Y, Cheng G, Li D, et al. Named entity recognition for mechanical design and manufacturing area. Comput Eng Appl, 2017, 53(20): 100 doi: 10.3778/j.issn.1002-8331.1604-0231
[18]	趙曉寧, 馮志鵬. 基于集合經驗模式分解和交叉能量算子的滾動軸承故障診斷. 工程科學學報, 2015, 37(S1):65 Zhao X N, Feng Z P. Fault diagnosis of rolling element bearing based on ensemble empirical mode decomposition and cross energy operator. Chin J Eng, 2015, 37(S1): 65
[19]	張東, 馮志鵬. 基于變分模式分解和微積分增強能量算子的滾動軸承故障診斷. 工程科學學報, 2016, 38(9):1327 Zhang D, Feng Z P. Fault diagnosis of rolling bearings based on variational mode decomposition and calculus enhanced energy operator. Chin J Eng, 2016, 38(9): 1327
[20]	趙萬華, 張星, 呂盾, 等. 國產數控機床的技術現狀與對策. 航空制造技術, 2016, 59(9):16 Zhao W H, Zhang X, Lv D, et al. Technical status and strategies for domestic CNC machine tools. Aeron Manuf Technol, 2016, 59(9): 16
[21]	Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality // Advances in Neural Information Processing Systems 26(NIPS 2013). Lake Tahoe, 2013: 3111
[22]	Lafferty J, McCallum A, Pereira F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data // Proceedings of the 18th International Conference on Machine Learning 2001. Williamstown, 2001: 282
[23]	袁莎, 唐杰, 顧曉韜. 開放互聯網中的學者畫像技術綜述. 計算機研究與發展, 2018, 55(9):1903 doi: 10.7544/issn1000-1239.2018.20180139 Yuan S, Tang J, Gu X T. A summary of scholars' portrait techniques in the open internet. J Comput Res Dev, 2018, 55(9): 1903 doi: 10.7544/issn1000-1239.2018.20180139
[24]	朱文球, 劉強. 基于條件隨機域的上下文人類動作識別. 計算機工程與應用, 2008, 44(28):180 doi: 10.3778/j.issn.1002-8331.2008.28.060 Zhu W Q, Liu Q. Conditional random fields with loop and its inference algorithm. Comput Eng Appl, 2008, 44(28): 180 doi: 10.3778/j.issn.1002-8331.2008.28.060
[25]	Lai S W. Word and document embeddings based on neural network approaches[J/OL]. arXiv preprint (2016-11-18)[2019-09-17]. https://arxiv.org/abs/1611.05962