{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T21:09:02Z","timestamp":1781125742468,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1934219; U1734211,"],"award-info":[{"award-number":["U1934219; U1734211,"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"National Science Fund for Excellent Young Scholars","doi-asserted-by":"publisher","award":["52022010"],"award-info":[{"award-number":["52022010"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science and Technology Research and Development Plan of China National Railway Corporation Limited","award":["2020G019"],"award-info":[{"award-number":["2020G019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.<\/jats:p>","DOI":"10.3390\/info12110485","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T23:22:28Z","timestamp":1638314548000},"page":"485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Predictive Maintenance for Switch Machine Based on Digital Twins"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6599-5526","authenticated-orcid":false,"given":"Jia","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"The National Engineering Research Center of Rail Transportation Operation Control System, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-1342","authenticated-orcid":false,"given":"Yongkui","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"The National Engineering Research Center of Rail Transportation Operation Control System, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"The National Engineering Research Center of Rail Transportation Operation Control System, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5702-1281","authenticated-orcid":false,"given":"Xiaoxi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.future.2021.02.014","article-title":"Tracking and collision avoidance of virtual coupling train control system","volume":"120","author":"Cao","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1063\/1.5085397","article-title":"Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system","volume":"29","author":"Cao","year":"2019","journal-title":"Chaos"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6331","DOI":"10.1109\/TVT.2019.2914936","article-title":"Bio-inspired speed curve optimization and sliding mode tracking control for subway trains","volume":"68","author":"Cao","year":"2019","journal-title":"IEEE Trans. Ehicular Technol."},{"key":"ref_4","first-page":"80","article-title":"Research on fault feature extraction and diagnosis of railway switches based on PLSA and SVM","volume":"40","author":"Zhong","year":"2018","journal-title":"J. China Railw. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.future.2018.05.038","article-title":"Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing","volume":"88","author":"Cao","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11184","DOI":"10.1109\/TVT.2021.3090419","article-title":"Sound based fault diagnosis for RPMs based on multi-scale fractional permutation entropy and two-scale algorithm","volume":"70","author":"Sun","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ardakani, H.D., Lucas, C., Siegel, D., Chang, S., Dersin, P., Bonnet, B., and Lee, J. (2012, January 23\u201325). PHM for railway system\u2014A case study on the health assessment of the point machines. Proceedings of the 2012 IEEE Conference on Prognostics and Health Management, Beijing, China.","DOI":"10.1109\/ICPHM.2012.6299533"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/MITS.2019.2926366","article-title":"A fault diagnosis method for train plug doors via sound signals","volume":"13","author":"Sun","year":"2021","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"28417","DOI":"10.1109\/ACCESS.2018.2836677","article-title":"IoT heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable industry 4.0","volume":"6","author":"Hortelano","year":"2018","journal-title":"IEEE Access"},{"key":"ref_10","first-page":"1901","article-title":"Prognostics with Autoregressive Moving Average for Railway Turnouts. Annual Conference of the PHM Society","volume":"2","author":"Guclu","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","first-page":"9","article-title":"Prediction research of point switch loophole by of polynomial fitting","volume":"11","author":"Liu","year":"2020","journal-title":"Heilongjiang Sci."},{"key":"ref_12","first-page":"880","article-title":"Prediction of landslide displacement time series based on support vector regression machine with artificial bee colony algorithm","volume":"27","author":"Yang","year":"2019","journal-title":"J. Eng. Geol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/S0040-1625(02)00195-6","article-title":"Applying the Grey prediction model to the global integrated circuit industry","volume":"70","author":"Hsu","year":"2003","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","article-title":"Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network","volume":"10","author":"Kong","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_15","unstructured":"Li, Q., Pei, H., Song, H., and Zhu, H. (2021, October 15). Prediction of Slope Displacement Based on PSO-SVR-NGM Combined with Entropy Weight Method. Available online: http:\/\/150.138.141.24\/kcms\/detail\/detail.aspx?filename=GCDZ2021062100C&dbcode=CJFD&dbname=CAPJ2021."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/0951192X.2018.1529430","article-title":"The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor","volume":"32","author":"Nikolaos","year":"2019","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_17","first-page":"753","article-title":"Connotation, architecture and trends of product digital twin","volume":"23","author":"Zhuang","year":"2017","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.jmsy.2020.01.007","article-title":"A review on the characteristics of cyber- physical systems for the future smart factories","volume":"54","author":"Napoleone","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/j.procir.2019.04.049","article-title":"Digital Twin for Machining Tool Condition Prediction","volume":"81","author":"Qiao","year":"2019","journal-title":"Procedia CIRP"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TII.2018.2873186","article-title":"Digital twin in industry: State-of-the-art","volume":"15","author":"Tao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"154798","DOI":"10.1155\/2011\/154798","article-title":"Reengineering Aircraft Structural Life Prediction Using a Digital Twin","volume":"2011","author":"Tuegel","year":"2011","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"930","DOI":"10.2514\/1.J055201","article-title":"Dynamic bayesian network for aircraft wing health monitoring digital twin","volume":"55","author":"Li","year":"2017","journal-title":"AIAA J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101974","DOI":"10.1016\/j.rcim.2020.101974","article-title":"A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin","volume":"65","author":"Luo","year":"2020","journal-title":"Robot.-Comput.-Integr. Manuf."},{"key":"ref_24","first-page":"657","article-title":"Digital Twin: A Machine Learning Approach to Predict Individual Stress Levels in Extreme Environments","volume":"Volume 8","author":"Scheuermann","year":"2020","journal-title":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers"},{"key":"ref_25","first-page":"815","article-title":"Health Prediction of Shearers Driven by Digital Twin and Deep Learning","volume":"31","author":"Ding","year":"2020","journal-title":"China Mech. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.cirp.2018.04.055","article-title":"Digital twin driven prognostics and health management for complex equipment","volume":"67","author":"Tao","year":"2018","journal-title":"CIRP Ann."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1007\/s00366-019-00921-y","article-title":"Long short-term memory for predicting daily suspended sediment concentration","volume":"37","author":"Kaveh","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11633-020-1276-6","article-title":"A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings","volume":"18","author":"Liu","year":"2021","journal-title":"Int. J. Autom. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","article-title":"ARIMA models to predict next-day electricity prices","volume":"18","author":"Contreras","year":"2003","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1017\/S0950268815001144","article-title":"Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model","volume":"144","author":"Liu","year":"2016","journal-title":"Epidemiol. Infect."},{"key":"ref_31","first-page":"744","article-title":"A Combination method for photovoltaic power forecasting based on entropy weight method","volume":"35","author":"Yang","year":"2014","journal-title":"Acta Energ. Solaris Sin."},{"key":"ref_32","first-page":"67","article-title":"Detection rod gap jamming fault at second traction point of S700K switch machine for urban rail transit","volume":"18","author":"Yang","year":"2021","journal-title":"Railw. Signal Commun. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.energy.2019.05.230","article-title":"Predicting residential energy consumption using CNN-LSTM neural networks","volume":"182","author":"Kim","year":"2019","journal-title":"Energy"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/11\/485\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:33:50Z","timestamp":1760168030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/11\/485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,22]]},"references-count":33,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["info12110485"],"URL":"https:\/\/doi.org\/10.3390\/info12110485","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,22]]}}}