Exploring Techniques for Building Language Models Targeted at Sewing Equipment Operation and Maintenance Management

Abstract

The intelligent operation and maintenance management of sewing equipment needs to solve the problem of information mining and language model construction of unstructured text, which is of great significance to improve the speed and accuracy of the diagnosis of equipment defects and faults, and realize the intelligent decision-making of equipment maintenance. In this paper, firstly, we propose a method based on bidirectional encoder representations from transformers-conditional random fields(BERT-CRF) to extract key entity information, such as device names and attributes. Then, through the relationship extraction model based on bidirectional gated recurrent unit-attention(BiGRU-Attention), the semantic association between entities is captured effectively to provide support for the construction of the sewing equipment knowledge graph(KG). According to the text analysis scenario of sewing equipment, the model is specially trained and optimized in the task scenarios of text entity recognition, information extraction and fault diagnosis of sewing equipment. Compared with existing deep learning algorithms, the proposed method achieves a 20% to 30% performance improvement on the validation and test sets, demonstrating significant advantages in the recall rate and the accuracy. To facilitate the mining of unstructured text information on sewing equipment, this study provides a reference for constructing a KG that integrates data on flat sewing equipment, including aspects of equipment fault operation, maintenance and flat sewing process route design.

Keywords

sewing equipment operation management / language model / entity extraction / relation extraction / knowledge graph

References

[1] XU Y N, THOMASSEY S, ZENG X Y. AI for apparel manufacturing in big data era:a focus on cutting and sewing[M]//Artificial Intelligence for Fashion Industry in the Big Data Era. Singapore: Springer, 2018:125-151.

[2] JIA J, YANG Q, FU H, et al. Construction of pre-trained language model and text semantic analysis based on power equipment big data[J]. Chinese Journal of Electrical Engineering, 2023, 43(3):1027-1037. (in Chinese)

[3] TAO J Q, LI X Y, ZHENG P, et al. State-of-the-art and frontier of manufacturing KG application[J]. Computer Integrated Manufacturing System, 2022, 28(12):3720-3736. (in Chinese)

[4] ZHANG D H, LIU Z Y, JIA W Q, et al. A review on knowledge graph and its application prospects to intelligent manufacturing[J]. Journal of Mechanical Engineering, 2021, 57(5):90-113. (in Chinese)

[5] SHEN X W, LI X Y, ZHOU B, et al. Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph[J]. Advanced Engineering Informatics, 2023,55:101880.

[6] HU Y, DING Y, Xu F, et al. Knowledge recommendation system for human-robot collaborative disassembly using knowledge graph[C]//ASME 16th International Manufacturing Science and Engineering Conference. New York: ASME, 2021:21-25.

[7] HEDBERG T D, MANAS B, CAMELIO J A. Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads[J]. Journal of Computing and Information Science in Engineering, 2020, 20(1):011011.

[8] HUET A, SEGONDS F, PINQUIE R, et al. Context-aware cognitive design assistant:implementation and study of design rules recommendations[J]. Advanced Engineering Informatics, 2021,50:101419.

[9] HAO Q, HU S L. Smart design on the flexible gear based on knowledge graph[J]. Journal of Physics:Conference Series, 2021, 1885(5):052021.

[10] XU D Z, JIN C, MA C, et al. Geographic named-entity recognition based on BERT-BiGRU-CRF and multi-head attention mechanism[J]. Network Security and Data Governance, 2023, 42(1):169-173. (in Chinese)

[11] ZHANG Q, ZHAO G Y, SU Y, et al. Power text semantic recognition algorithm based on improved Bert-AutoML[J]. Electronic Design Engineering, 2024, 32(4):43-46,51. (in Chinese)

[12] GAO G Z, LI Y, HUA Y P, et al. Named entity recognition in oil and gas domain based on BERT-BiLSTM-CRF model[J]. Journal of Changjiang University, 2024, 21(1):57-65. (in Chinese)

[13] DEVLIN J, CHANG M W, LEE K, et al. BERT:pre-training of deep bidirectional transformers for language understanding[C]//The North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Human Language Technologies. Stroudsburg:ACL, 2019:4171-4186.

[14] Li F, PAN H S, SHENG S X, et al. Image retrieval based on vision transformer and masked learning[J]. Journal of Donghua University(English Edition), 2023, 40(5):539-547.

[15] ZHOU H R, SONG H, GONG M M. Semantic path attention network based on heterogeneous graphs for natural language to SQL task[J]. Journal of Donghua University(English Edition), 2023, 40(5):531-538.

[16] ZHENG L R, XIAO X X, ZOU B J, et al. Named entity recognition for electronic medical record based on BERT[J]. Computer and Modernization, 2024,1:87-91. (in Chinese)

[17] YANG Y P, WANG S T. Study on malicious traffic classification algorithm based on CNN combined with BiGRU[J/OL]. Computer Science, 2024:1-9[2024-01-26]. https://kns.cnki.net/kcms/detail/50.1075.TP.20240124.1838.002.html. in Chinese)

[18] LAN W W, XU W. Character-based neural networks for sentence pair modeling[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. ACL: ACL, 2018:157-162.