A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine
A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/1891453 |
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| _version_ | 1832560478290706432 |
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| author | Mingliang Liang Dongmin Su Daidi Hu Mingtao Ge |
| author_facet | Mingliang Liang Dongmin Su Daidi Hu Mingtao Ge |
| author_sort | Mingliang Liang |
| collection | DOAJ |
| description | A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods. |
| format | Article |
| id | doaj-art-6f827c6d610c4feba539954b2484a074 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6f827c6d610c4feba539954b2484a0742025-02-03T01:27:28ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/18914531891453A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning MachineMingliang Liang0Dongmin Su1Daidi Hu2Mingtao Ge3Zhengzhou Railway Vocational & Technical College, Zhengzhou 451460, ChinaZhengzhou Railway Vocational & Technical College, Zhengzhou 451460, ChinaCollege of Electronics and Information Engineering, SIAS International University, Xinzheng 451150, ChinaCollege of Electronics and Information Engineering, SIAS International University, Xinzheng 451150, ChinaA rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.http://dx.doi.org/10.1155/2018/1891453 |
| spellingShingle | Mingliang Liang Dongmin Su Daidi Hu Mingtao Ge A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine Shock and Vibration |
| title | A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine |
| title_full | A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine |
| title_fullStr | A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine |
| title_full_unstemmed | A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine |
| title_short | A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine |
| title_sort | novel faults diagnosis method for rolling element bearings based on elcd and extreme learning machine |
| url | http://dx.doi.org/10.1155/2018/1891453 |
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