Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework
In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the rel...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/9910601 |
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| _version_ | 1832550150962151424 |
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| author | Jia-Qi Liu Yun-Wen Feng Cheng Lu Wei-Huang Pan Da Teng |
| author_facet | Jia-Qi Liu Yun-Wen Feng Cheng Lu Wei-Huang Pan Da Teng |
| author_sort | Jia-Qi Liu |
| collection | DOAJ |
| description | In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design. |
| format | Article |
| id | doaj-art-87e1b93b715e4d909a3c2b039916051a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-87e1b93b715e4d909a3c2b039916051a2025-02-03T06:07:38ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/99106019910601Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling FrameworkJia-Qi Liu0Yun-Wen Feng1Cheng Lu2Wei-Huang Pan3Da Teng4School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaIn order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design.http://dx.doi.org/10.1155/2021/9910601 |
| spellingShingle | Jia-Qi Liu Yun-Wen Feng Cheng Lu Wei-Huang Pan Da Teng Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework Shock and Vibration |
| title | Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework |
| title_full | Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework |
| title_fullStr | Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework |
| title_full_unstemmed | Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework |
| title_short | Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework |
| title_sort | vibration reliability analysis of aeroengine rotor based on intelligent neural network modeling framework |
| url | http://dx.doi.org/10.1155/2021/9910601 |
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