Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion
The assembly, as the core part of low-pressure rotor manufacturing, is of great importance to ensure its unbalance. Low-voltage rotor assembly is a multi-process process influenced by the quality of part machining, assembly process, and assembly quality, resulting in unbalance that is difficult to p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/10/936 |
| _version_ | 1797431502772895744 |
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| author | Mingwei Wang Huibin Zhang Lei Liu Jingtao Zhou Lu Yao Xin Ma Manxian Wang |
| author_facet | Mingwei Wang Huibin Zhang Lei Liu Jingtao Zhou Lu Yao Xin Ma Manxian Wang |
| author_sort | Mingwei Wang |
| collection | DOAJ |
| description | The assembly, as the core part of low-pressure rotor manufacturing, is of great importance to ensure its unbalance. Low-voltage rotor assembly is a multi-process process influenced by the quality of part machining, assembly process, and assembly quality, resulting in unbalance that is difficult to predict during the assembly process. The unbalance measurement in the assembly process is important for the subsequent process optimization. Therefore, in order to achieve the prediction of unbalance measurement in the assembly process, this paper proposes an unbalance measurement prediction method based on mechanism and data fusion. Firstly, through research and analysis, the influencing factors of unbalance are determined, the low-pressure rotor blade sequencing mechanism model is established, and the blade sequencing optimization is realized by using reinforcement learning. Then, since the unbalance is formed after all the processes are completed and the subsequent work steps in the assembly process have not been carried out yet, the actual process parameters cannot be obtained, the semi-physical simulation method is used to combine the actual data of the assembled work steps with the theoretical data of the unassembled work steps to build a prediction model of the unbalance based on the BRNN (bidirectional recurrent neural network) network to achieve the prediction of the unbalance measurement in the assembly process. Finally, the model was validated using actual assembly process data, which proved the feasibility and effectiveness of the method. |
| first_indexed | 2024-03-09T09:46:00Z |
| format | Article |
| id | doaj.art-ac51e87f51cd4ea98ed4d1c6d87c9d69 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T09:46:00Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-ac51e87f51cd4ea98ed4d1c6d87c9d692023-12-02T00:35:53ZengMDPI AGMachines2075-17022022-10-01101093610.3390/machines10100936Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data FusionMingwei Wang0Huibin Zhang1Lei Liu2Jingtao Zhou3Lu Yao4Xin Ma5Manxian Wang6Key Laboratory of High Performance Manufacturing for Aero Engine, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, ChinaKey Laboratory of High Performance Manufacturing for Aero Engine, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an 710072, ChinaAECC XI’AN AERO-ENGINE Ltd., Xi’an 710072, ChinaThe assembly, as the core part of low-pressure rotor manufacturing, is of great importance to ensure its unbalance. Low-voltage rotor assembly is a multi-process process influenced by the quality of part machining, assembly process, and assembly quality, resulting in unbalance that is difficult to predict during the assembly process. The unbalance measurement in the assembly process is important for the subsequent process optimization. Therefore, in order to achieve the prediction of unbalance measurement in the assembly process, this paper proposes an unbalance measurement prediction method based on mechanism and data fusion. Firstly, through research and analysis, the influencing factors of unbalance are determined, the low-pressure rotor blade sequencing mechanism model is established, and the blade sequencing optimization is realized by using reinforcement learning. Then, since the unbalance is formed after all the processes are completed and the subsequent work steps in the assembly process have not been carried out yet, the actual process parameters cannot be obtained, the semi-physical simulation method is used to combine the actual data of the assembled work steps with the theoretical data of the unassembled work steps to build a prediction model of the unbalance based on the BRNN (bidirectional recurrent neural network) network to achieve the prediction of the unbalance measurement in the assembly process. Finally, the model was validated using actual assembly process data, which proved the feasibility and effectiveness of the method.https://www.mdpi.com/2075-1702/10/10/936mechanism modeldata-drivensemi-physical simulationunbalance forecastaeroengine assembly |
| spellingShingle | Mingwei Wang Huibin Zhang Lei Liu Jingtao Zhou Lu Yao Xin Ma Manxian Wang Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion Machines mechanism model data-driven semi-physical simulation unbalance forecast aeroengine assembly |
| title | Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion |
| title_full | Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion |
| title_fullStr | Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion |
| title_full_unstemmed | Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion |
| title_short | Unbalance Prediction of Low Pressure Rotor Based on Mechanism and Data Fusion |
| title_sort | unbalance prediction of low pressure rotor based on mechanism and data fusion |
| topic | mechanism model data-driven semi-physical simulation unbalance forecast aeroengine assembly |
| url | https://www.mdpi.com/2075-1702/10/10/936 |
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