Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM
Aiming at the problem of complex modulation characteristics of vibration signals of the planetary gearbox make the state identification model low accuracy,a state identification method for key components of planetary gearbox based on combinations of ensemble local mean decomposition（ ELMD） energy en...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Transmission
2018-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.034 |
| _version_ | 1797819199811223552 |
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| author | Zhang Luyang Qin Bo Yin Heng Wang Jianguo |
| author_facet | Zhang Luyang Qin Bo Yin Heng Wang Jianguo |
| author_sort | Zhang Luyang |
| collection | DOAJ |
| description | Aiming at the problem of complex modulation characteristics of vibration signals of the planetary gearbox make the state identification model low accuracy,a state identification method for key components of planetary gearbox based on combinations of ensemble local mean decomposition（ ELMD） energy entropy and artificial fish swarm algorithm finding support vector machine（ AFSA-SVM） optimal kernel function coefficient is proposed. To begin,a number of narrow-band product function（ PF） from vibration signals are obtained by ELMD with after morphological average filter. Then the high dimensional feature vector set is built by calculating the energy entropy of the above PF. At last,the fault diagnosis model is developed based on AFSA-SVM algorithm,in which the extracted fault features are employed as inputs. The experimental results show that the proposed method can show the fault component of the original signal with effectively. It has the state identification accurate of the model is greatly improved. |
| first_indexed | 2024-03-13T09:19:27Z |
| format | Article |
| id | doaj.art-aa855893a2964e31a4800c5830754cc2 |
| institution | Directory Open Access Journal |
| issn | 1004-2539 |
| language | zho |
| last_indexed | 2024-03-13T09:19:27Z |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj.art-aa855893a2964e31a4800c5830754cc22023-05-26T09:48:32ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014216417029937045Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVMZhang LuyangQin BoYin HengWang JianguoAiming at the problem of complex modulation characteristics of vibration signals of the planetary gearbox make the state identification model low accuracy,a state identification method for key components of planetary gearbox based on combinations of ensemble local mean decomposition（ ELMD） energy entropy and artificial fish swarm algorithm finding support vector machine（ AFSA-SVM） optimal kernel function coefficient is proposed. To begin,a number of narrow-band product function（ PF） from vibration signals are obtained by ELMD with after morphological average filter. Then the high dimensional feature vector set is built by calculating the energy entropy of the above PF. At last,the fault diagnosis model is developed based on AFSA-SVM algorithm,in which the extracted fault features are employed as inputs. The experimental results show that the proposed method can show the fault component of the original signal with effectively. It has the state identification accurate of the model is greatly improved.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.034ELMD energy entropy;Planetary gearbox;Optimal kernel function coefficient;AFSA-SVM |
| spellingShingle | Zhang Luyang Qin Bo Yin Heng Wang Jianguo Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM Jixie chuandong ELMD energy entropy;Planetary gearbox;Optimal kernel function coefficient;AFSA-SVM |
| title | Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM |
| title_full | Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM |
| title_fullStr | Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM |
| title_full_unstemmed | Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM |
| title_short | Fault Diagnosis of Planetary Gearbox Key Component based ELMD Energy Entropy and AFSA-SVM |
| title_sort | fault diagnosis of planetary gearbox key component based elmd energy entropy and afsa svm |
| topic | ELMD energy entropy;Planetary gearbox;Optimal kernel function coefficient;AFSA-SVM |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.06.034 |
| work_keys_str_mv | AT zhangluyang faultdiagnosisofplanetarygearboxkeycomponentbasedelmdenergyentropyandafsasvm AT qinbo faultdiagnosisofplanetarygearboxkeycomponentbasedelmdenergyentropyandafsasvm AT yinheng faultdiagnosisofplanetarygearboxkeycomponentbasedelmdenergyentropyandafsasvm AT wangjianguo faultdiagnosisofplanetarygearboxkeycomponentbasedelmdenergyentropyandafsasvm |