Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network
Clarifying the noise source and the contribution of each path is essential for the system’s noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using kno...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12244 |
| _version_ | 1827640715321016320 |
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| author | Yizhe Huang Bin Huang Yuanpeng Cao Xin Zhan Qibai Huang Jiaxuan Wang |
| author_facet | Yizhe Huang Bin Huang Yuanpeng Cao Xin Zhan Qibai Huang Jiaxuan Wang |
| author_sort | Yizhe Huang |
| collection | DOAJ |
| description | Clarifying the noise source and the contribution of each path is essential for the system’s noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using known transfer functions and target point test values when identifying noise sources. This article suggests a method for diagnosing noise using transfer path analysis and neural networks (TPA-NN). Firstly, the principle and scheme for analyzing the transmission path of the converter cabinet are proposed. The transfer function of each path is obtained by selecting suitable path points, reference points, and target points for air and structure acoustic vibration experiments. The external target point data are then combined with the neural network’s linear fitting function, and the contribution of each path is used as an output for network training while some path point contributions are rebuilt. The results indicate that the method’s outcomes are most accurate when the converter cabinet’s path point is 13 and the target point is 6. This approach offers an innovative technique for locating noise sources in intricate systems. |
| first_indexed | 2024-03-09T17:03:31Z |
| format | Article |
| id | doaj.art-674f1b68c6304329af138cbd6c3f90ca |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:03:31Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-674f1b68c6304329af138cbd6c3f90ca2023-11-24T14:26:49ZengMDPI AGApplied Sciences2076-34172023-11-0113221224410.3390/app132212244Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural NetworkYizhe Huang0Bin Huang1Yuanpeng Cao2Xin Zhan3Qibai Huang4Jiaxuan Wang5Dongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, ChinaHubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaWuhan Second Ship Design and Research Institute, Wuhan 430205, ChinaDongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaClarifying the noise source and the contribution of each path is essential for the system’s noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using known transfer functions and target point test values when identifying noise sources. This article suggests a method for diagnosing noise using transfer path analysis and neural networks (TPA-NN). Firstly, the principle and scheme for analyzing the transmission path of the converter cabinet are proposed. The transfer function of each path is obtained by selecting suitable path points, reference points, and target points for air and structure acoustic vibration experiments. The external target point data are then combined with the neural network’s linear fitting function, and the contribution of each path is used as an output for network training while some path point contributions are rebuilt. The results indicate that the method’s outcomes are most accurate when the converter cabinet’s path point is 13 and the target point is 6. This approach offers an innovative technique for locating noise sources in intricate systems.https://www.mdpi.com/2076-3417/13/22/12244noise source identificationtransfer path analysisneural networkpath contribution |
| spellingShingle | Yizhe Huang Bin Huang Yuanpeng Cao Xin Zhan Qibai Huang Jiaxuan Wang Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network Applied Sciences noise source identification transfer path analysis neural network path contribution |
| title | Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network |
| title_full | Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network |
| title_fullStr | Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network |
| title_full_unstemmed | Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network |
| title_short | Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network |
| title_sort | noise source diagnosis method based on transfer path analysis and neural network |
| topic | noise source identification transfer path analysis neural network path contribution |
| url | https://www.mdpi.com/2076-3417/13/22/12244 |
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