Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox
According to the nonlinear and non-stationary of the planetary gearbox vibration signal fault characteristics is difficult to extract and the problem of low classification accuracy Gaussian kernel extreme learning machine based on random generation kernel parameters， a method for identifying the sta...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2019-01-01
|
| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.05.028 |
| _version_ | 1797818964684832768 |
|---|---|
| author | Bo Qin Heng Yin Zhuo Wang Jianqiang Zhang Zhijun Li Jianguo Wang |
| author_facet | Bo Qin Heng Yin Zhuo Wang Jianqiang Zhang Zhijun Li Jianguo Wang |
| author_sort | Bo Qin |
| collection | DOAJ |
| description | According to the nonlinear and non-stationary of the planetary gearbox vibration signal fault characteristics is difficult to extract and the problem of low classification accuracy Gaussian kernel extreme learning machine based on random generation kernel parameters， a method for identifying the state of planetary gearboxes with enhence multi-scale permutation entropy （EMPE） and nuclear-polarized Gaussian kernel extreme learning machine （KELM） is proposed. Firstly， noise reduction of vibration signals of planetary gearbox planetary gears by morphological average filtering and using EMPE to obtain permutation entropy values at multiple scales to constructing eigenvector set. Secondly， kernel parameter <italic>σ</italic> of Gaussian kernel extreme learning machine is optimized by kernel polarization. Finally， using the EMPE eigenvector set as input， the planetary gear state identification model is constructed by the training of KP-KELM algorithm. The experiment results show that， compared with the fault classification model based on SVM and KELM， the EMPE and KP-KELM planetary gear fault diagnosis method has higher classification accuracy and stronger generalization ability. |
| first_indexed | 2024-03-13T09:16:54Z |
| format | Article |
| id | doaj.art-2737b0ee05c5452c94e39d1ec8bd0f89 |
| institution | Directory Open Access Journal |
| issn | 1004-2539 |
| language | zho |
| last_indexed | 2024-03-13T09:16:54Z |
| publishDate | 2019-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj.art-2737b0ee05c5452c94e39d1ec8bd0f892023-05-26T09:53:59ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392019-01-014314615130645189Application of EMPE and KP-KELM in Fault Diagnosis of Planetary GearboxBo QinHeng YinZhuo WangJianqiang ZhangZhijun LiJianguo WangAccording to the nonlinear and non-stationary of the planetary gearbox vibration signal fault characteristics is difficult to extract and the problem of low classification accuracy Gaussian kernel extreme learning machine based on random generation kernel parameters， a method for identifying the state of planetary gearboxes with enhence multi-scale permutation entropy （EMPE） and nuclear-polarized Gaussian kernel extreme learning machine （KELM） is proposed. Firstly， noise reduction of vibration signals of planetary gearbox planetary gears by morphological average filtering and using EMPE to obtain permutation entropy values at multiple scales to constructing eigenvector set. Secondly， kernel parameter <italic>σ</italic> of Gaussian kernel extreme learning machine is optimized by kernel polarization. Finally， using the EMPE eigenvector set as input， the planetary gear state identification model is constructed by the training of KP-KELM algorithm. The experiment results show that， compared with the fault classification model based on SVM and KELM， the EMPE and KP-KELM planetary gear fault diagnosis method has higher classification accuracy and stronger generalization ability.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.05.028Planetary gearbox;Multi resolution feature extraction;Kernel parameter optimization;State identification |
| spellingShingle | Bo Qin Heng Yin Zhuo Wang Jianqiang Zhang Zhijun Li Jianguo Wang Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox Jixie chuandong Planetary gearbox;Multi resolution feature extraction;Kernel parameter optimization;State identification |
| title | Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox |
| title_full | Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox |
| title_fullStr | Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox |
| title_full_unstemmed | Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox |
| title_short | Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox |
| title_sort | application of empe and kp kelm in fault diagnosis of planetary gearbox |
| topic | Planetary gearbox;Multi resolution feature extraction;Kernel parameter optimization;State identification |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.05.028 |
| work_keys_str_mv | AT boqin applicationofempeandkpkelminfaultdiagnosisofplanetarygearbox AT hengyin applicationofempeandkpkelminfaultdiagnosisofplanetarygearbox AT zhuowang applicationofempeandkpkelminfaultdiagnosisofplanetarygearbox AT jianqiangzhang applicationofempeandkpkelminfaultdiagnosisofplanetarygearbox AT zhijunli applicationofempeandkpkelminfaultdiagnosisofplanetarygearbox AT jianguowang applicationofempeandkpkelminfaultdiagnosisofplanetarygearbox |