Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation
In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a sing...
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MDPI AG
2025-04-01
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| author | Qiang Zhang Zhe Wu Boshuo An Ruitian Sun Yanping Cui |
| author_facet | Qiang Zhang Zhe Wu Boshuo An Ruitian Sun Yanping Cui |
| author_sort | Qiang Zhang |
| collection | DOAJ |
| description | In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R<sup>2</sup>) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. |
| format | Article |
| id | doaj-art-2f7e6566f55a4c63a61da23d7cc42d02 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2f7e6566f55a4c63a61da23d7cc42d022025-08-20T03:52:56ZengMDPI AGSensors1424-82202025-04-01259277510.3390/s25092775Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance EvaluationQiang Zhang0Zhe Wu1Boshuo An2Ruitian Sun3Yanping Cui4Key Laboratory of Vehicle Transmission, China North Vehicle Research Institute, Beijing 100072, ChinaSchool of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaIn the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R<sup>2</sup>) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance.https://www.mdpi.com/1424-8220/25/9/2775dynamic dataartificial intelligencefault diagnosisgearboxintegrated form-performance |
| spellingShingle | Qiang Zhang Zhe Wu Boshuo An Ruitian Sun Yanping Cui Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation Sensors dynamic data artificial intelligence fault diagnosis gearbox integrated form-performance |
| title | Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation |
| title_full | Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation |
| title_fullStr | Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation |
| title_full_unstemmed | Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation |
| title_short | Digital Twin-Based Technical Research on Comprehensive Gear Fault Diagnosis and Structural Performance Evaluation |
| title_sort | digital twin based technical research on comprehensive gear fault diagnosis and structural performance evaluation |
| topic | dynamic data artificial intelligence fault diagnosis gearbox integrated form-performance |
| url | https://www.mdpi.com/1424-8220/25/9/2775 |
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