Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction
Accurate temperature prediction is critical for ensuring mechanical stability and operational safety during complex operating conditions and long-term operation of rolling bearings. This study proposes a digital twin (DT) system for bearing thermal analysis and a digital twin-driven virtual–real hyb...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6671 |
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| author | Yu Wang Qingbin Tong Luqiang Yang Junci Cao Jiang Cao |
| author_facet | Yu Wang Qingbin Tong Luqiang Yang Junci Cao Jiang Cao |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | Accurate temperature prediction is critical for ensuring mechanical stability and operational safety during complex operating conditions and long-term operation of rolling bearings. This study proposes a digital twin (DT) system for bearing thermal analysis and a digital twin-driven virtual–real hybrid framework, achieving thermal prediction from low-risk behaviors (low-speed/light-load) to high-risk behaviors (high-speed/heavy-load). To address the time-varying and ambiguous parameters, an efficient Nutcracker Optimization Algorithm (NOA)-based identification mechanism is introduced to dynamically calibrate the virtual thermal model, overcoming the limitations of static modeling and data isolation inherent in conventional thermal analysis methods. The Euclidean distance and uncertainty analysis between real temperature and predicted temperature demonstrate the highly reliable predictive ability of the proposed framework in terms of bearing thermal, especially under variable speed conditions. The proposed framework has certain guiding significance for enhancing thermal safety and fault early-warning capabilities of bearings during long-term operation. |
| format | Article |
| id | doaj-art-fe12e255c8164a96a07dfcc7b5fa5e28 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fe12e255c8164a96a07dfcc7b5fa5e282025-08-20T02:24:30ZengMDPI AGApplied Sciences2076-34172025-06-011512667110.3390/app15126671Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal PredictionYu Wang0Qingbin Tong1Luqiang Yang2Junci Cao3Jiang Cao4School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaLuoyang Bearing Research Institute Co., Ltd., Luoyang 471039, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaCRRC Qingdao Sifang Co., Ltd., Qingdao 266111, ChinaAccurate temperature prediction is critical for ensuring mechanical stability and operational safety during complex operating conditions and long-term operation of rolling bearings. This study proposes a digital twin (DT) system for bearing thermal analysis and a digital twin-driven virtual–real hybrid framework, achieving thermal prediction from low-risk behaviors (low-speed/light-load) to high-risk behaviors (high-speed/heavy-load). To address the time-varying and ambiguous parameters, an efficient Nutcracker Optimization Algorithm (NOA)-based identification mechanism is introduced to dynamically calibrate the virtual thermal model, overcoming the limitations of static modeling and data isolation inherent in conventional thermal analysis methods. The Euclidean distance and uncertainty analysis between real temperature and predicted temperature demonstrate the highly reliable predictive ability of the proposed framework in terms of bearing thermal, especially under variable speed conditions. The proposed framework has certain guiding significance for enhancing thermal safety and fault early-warning capabilities of bearings during long-term operation.https://www.mdpi.com/2076-3417/15/12/6671digital twinthermal predictionparameter identificationrolling bearings |
| spellingShingle | Yu Wang Qingbin Tong Luqiang Yang Junci Cao Jiang Cao Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction Applied Sciences digital twin thermal prediction parameter identification rolling bearings |
| title | Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction |
| title_full | Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction |
| title_fullStr | Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction |
| title_full_unstemmed | Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction |
| title_short | Digital Twin-Driven Virtual–Real Hybrid Framework Based on Parameter Identification for Bearing Thermal Prediction |
| title_sort | digital twin driven virtual real hybrid framework based on parameter identification for bearing thermal prediction |
| topic | digital twin thermal prediction parameter identification rolling bearings |
| url | https://www.mdpi.com/2076-3417/15/12/6671 |
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