{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:08Z","timestamp":1777705028921,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from \u201cregular maintenance\u201d and \u201cpost-event maintenance\u201d to \u201cpredictive maintenance\u201d. In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of \u201ccombining the real with the virtual and controlling the real\u201d. Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom.<\/jats:p>","DOI":"10.3233\/jifs-233863","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T10:55:31Z","timestamp":1709031331000},"page":"9409-9430","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based digital twin for intelligent predictive maintenance of rapier loom"],"prefix":"10.1177","volume":"46","author":[{"given":"Yanjun","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"},{"name":"Career Leader Intelligent Control Automation Company, Suqian, Jiangsu Province, China"},{"name":"Tianjin Key Lab Power Transmiss & Safety Technol, Department State Key Lab Reliabil & Intellectual Elect, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoliang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiling","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-233863_ref1","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TR.2016.2570568","article-title":"A model-based method for remaining useful life prediction of machinery","volume":"65","author":"Lei","year":"2016","journal-title":"IEEE Transactions on Reliability"},{"key":"10.3233\/JIFS-233863_ref2","unstructured":"QG, Z. 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Hc and C Z, Reliability assessment of long-life products based on semi-parametric degradation model, Systems Engineering & Electronics 48 (2022), 75\u201381."},{"key":"10.3233\/JIFS-233863_ref4","unstructured":"L X, Research on warp knitting machine fault prediction alarm system based on convolutional neural network, Advances in Textile Science & Technology (02) (2019), 12\u20134."},{"key":"10.3233\/JIFS-233863_ref5","doi-asserted-by":"crossref","unstructured":"Sharma D.K. , Brahmachari S. , Singhal K. , et al., Data driven predictive maintenance applications for industrial systems with temporal convolutional networks, Computers & Industrial Engineering 169 (2022).","DOI":"10.1016\/j.cie.2022.108213"},{"key":"10.3233\/JIFS-233863_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compind.2018.07.002","article-title":"A deep learning driven method for fault classification and degradation assessment in mechanical equipment","volume":"104","author":"Li","year":"2019","journal-title":"Computers in Industry"},{"key":"10.3233\/JIFS-233863_ref7","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2022.02.032","article-title":"A hierarchical scheme for remaining useful life prediction with long short-term memory networks","volume":"487","author":"Song","year":"2022","journal-title":"Neurocomputing"},{"issue":"17","key":"10.3233\/JIFS-233863_ref8","doi-asserted-by":"crossref","first-page":"5238","DOI":"10.1080\/00207543.2020.1714091","article-title":"Digital twin of composite assembly manufacturing process[J]","volume":"58","author":"Polini","year":"2020","journal-title":"International Journal of Production Research"},{"issue":"1","key":"10.3233\/JIFS-233863_ref9","first-page":"012077","article-title":"Digital Twin concept for smart injection molding[C]","volume":"324","author":"Liau","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"issue":"11","key":"10.3233\/JIFS-233863_ref10","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.procir.2019.03.072","article-title":"Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance","volume":"59","author":"Aivaliotis","year":"2019","journal-title":"Procedia Cirp"},{"issue":"21","key":"10.3233\/JIFS-233863_ref11","doi-asserted-by":"crossref","first-page":"6471","DOI":"10.1080\/00207543.2020.1817999","article-title":"Angelica S. 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