Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN
In order to solve the problem that the complex structure and operating conditions of planetary gear box lead to the difficulty of signal fault feature extraction， the frequency spectrum feature of gearbox vibration signal with faults is preliminarily deduced by analyzing the vibration mechanism of p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2022-04-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.04.011 |
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| _version_ | 1841548819687276544 |
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| author | Pan Zheng Jianhua Zhou Sujie Gao Ben Chen Xiangxiong Liu Shijing Wu |
| author_facet | Pan Zheng Jianhua Zhou Sujie Gao Ben Chen Xiangxiong Liu Shijing Wu |
| author_sort | Pan Zheng |
| collection | DOAJ |
| description | In order to solve the problem that the complex structure and operating conditions of planetary gear box lead to the difficulty of signal fault feature extraction， the frequency spectrum feature of gearbox vibration signal with faults is preliminarily deduced by analyzing the vibration mechanism of planetary gear train. The method of harmonic product spectrum （HPS） and sideband product spectrum （SPS） is used to accurately extract the fault characteristic frequencies of the simulation signals under the condition that the noise interference and fault impact are not obvious. The vibration signals of the planetary gearbox under different operating conditions and different fault states are further collected，and the extracted fault features are input into the convolutional neural network for fault identification. The fault information of the gearbox is obtained successfully，which proves the feasibility of the proposed method in fault diagnosis of the planetary gearbox. |
| format | Article |
| id | doaj-art-3bcd92d51a46494b89c054c0807677b2 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2022-04-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-3bcd92d51a46494b89c054c0807677b22025-01-10T13:59:33ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392022-04-0146737930519065Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNNPan ZhengJianhua ZhouSujie GaoBen ChenXiangxiong LiuShijing WuIn order to solve the problem that the complex structure and operating conditions of planetary gear box lead to the difficulty of signal fault feature extraction， the frequency spectrum feature of gearbox vibration signal with faults is preliminarily deduced by analyzing the vibration mechanism of planetary gear train. The method of harmonic product spectrum （HPS） and sideband product spectrum （SPS） is used to accurately extract the fault characteristic frequencies of the simulation signals under the condition that the noise interference and fault impact are not obvious. The vibration signals of the planetary gearbox under different operating conditions and different fault states are further collected，and the extracted fault features are input into the convolutional neural network for fault identification. The fault information of the gearbox is obtained successfully，which proves the feasibility of the proposed method in fault diagnosis of the planetary gearbox.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.04.011Planetary gear trainFault feature extractionHarmonic product spectrumSideband product spectrumConvolutional neural network |
| spellingShingle | Pan Zheng Jianhua Zhou Sujie Gao Ben Chen Xiangxiong Liu Shijing Wu Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN Jixie chuandong Planetary gear train Fault feature extraction Harmonic product spectrum Sideband product spectrum Convolutional neural network |
| title | Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN |
| title_full | Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN |
| title_fullStr | Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN |
| title_full_unstemmed | Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN |
| title_short | Research of Fault Feature Extraction and Diagnosis of Planetary Gear Train based on SPS and CNN |
| title_sort | research of fault feature extraction and diagnosis of planetary gear train based on sps and cnn |
| topic | Planetary gear train Fault feature extraction Harmonic product spectrum Sideband product spectrum Convolutional neural network |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.04.011 |
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