Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference
The vehicle gearbox has a bad working environment and the fault mode is difficult to identify. On the basis of existing methods， a method based on two-layer-mode decomposition （TMD） and singular value decomposition （SVD） is proposed， combined with particle swarm （POS）-BP neural network for fault dia...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2021-05-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.05.025 |
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| author | Lei He Suqi Liu |
| author_facet | Lei He Suqi Liu |
| author_sort | Lei He |
| collection | DOAJ |
| description | The vehicle gearbox has a bad working environment and the fault mode is difficult to identify. On the basis of existing methods， a method based on two-layer-mode decomposition （TMD） and singular value decomposition （SVD） is proposed， combined with particle swarm （POS）-BP neural network for fault diagnosis. Firstly， the vibration signals under four typical conditions of normal transmission， rolling failure， outer ring crack and gear wear are collected on a self-built experimental platform. Then， the first 5 IMFs components of the signal is decomposed by EMD， since the spectrum of IMF1 is still complicated， the wavelet packet is used to continue the 2-layer decomposition. Finally， the eight sub-sequences are obtained by TMD， and the signal component matrix is constructed. Then， the singular value （SVD） of the component matrix is extracted as the eigenvalue， the eigenvalues are entered into the constructed POS-BP neural network diagnostic model， and the gearbox fault type is identified based on the output. The analysis results show that the method can be effectively applied to the fault diagnosis of special vehicle gearboxes， and the diagnostic accuracy rate reaches 92%， which provides an effective reference for gearbox state recognition under complex conditions. |
| format | Article |
| id | doaj-art-b9e82e3815834258968994842c933642 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-05-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-b9e82e3815834258968994842c9336422025-01-10T14:49:12ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-05-014516917610227835Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong InterferenceLei HeSuqi LiuThe vehicle gearbox has a bad working environment and the fault mode is difficult to identify. On the basis of existing methods， a method based on two-layer-mode decomposition （TMD） and singular value decomposition （SVD） is proposed， combined with particle swarm （POS）-BP neural network for fault diagnosis. Firstly， the vibration signals under four typical conditions of normal transmission， rolling failure， outer ring crack and gear wear are collected on a self-built experimental platform. Then， the first 5 IMFs components of the signal is decomposed by EMD， since the spectrum of IMF1 is still complicated， the wavelet packet is used to continue the 2-layer decomposition. Finally， the eight sub-sequences are obtained by TMD， and the signal component matrix is constructed. Then， the singular value （SVD） of the component matrix is extracted as the eigenvalue， the eigenvalues are entered into the constructed POS-BP neural network diagnostic model， and the gearbox fault type is identified based on the output. The analysis results show that the method can be effectively applied to the fault diagnosis of special vehicle gearboxes， and the diagnostic accuracy rate reaches 92%， which provides an effective reference for gearbox state recognition under complex conditions.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.05.025Two-layer-mode decomposition（TMD）Singular value decomposition（SVD）POS-BP neural networkFault diagnosis |
| spellingShingle | Lei He Suqi Liu Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference Jixie chuandong Two-layer-mode decomposition（TMD） Singular value decomposition（SVD） POS-BP neural network Fault diagnosis |
| title | Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference |
| title_full | Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference |
| title_fullStr | Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference |
| title_full_unstemmed | Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference |
| title_short | Gearbox Status Recognition based on TMD-SVD and POS-BP Networks Under Strong Interference |
| title_sort | gearbox status recognition based on tmd svd and pos bp networks under strong interference |
| topic | Two-layer-mode decomposition（TMD） Singular value decomposition（SVD） POS-BP neural network Fault diagnosis |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.05.025 |
| work_keys_str_mv | AT leihe gearboxstatusrecognitionbasedontmdsvdandposbpnetworksunderstronginterference AT suqiliu gearboxstatusrecognitionbasedontmdsvdandposbpnetworksunderstronginterference |