A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis
文献类型：期刊论文
| 作者 | Li, D. Z.; Zheng, X. ; Xie, Q. W.; Jin, Q. B.
|
| 刊名 | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING
 |
| 出版日期 | 2018-12-01 |
| 卷号 | 232期号:6页码:635-649 |
| 关键词 | Fault identification classification rolling bearing discrete wavelet transform phase space reconstruction singular value decomposition improved extreme learning machine |
| ISSN号 | 0954-4089 |
| DOI | 10.1177/0954408917733130 |
| 通讯作者 | Li, D. Z.(lidz@mail.buct.edu.cn) |
| 英文摘要 | A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis. |
| WOS关键词 | CLASSIFICATION ; TIME ; SVM |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000452309700001 |
| 出版者 | SAGE PUBLICATIONS LTD |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25673]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Li, D. Z. |
| 作者单位 | Beijing Univ Chem Technol, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, D. Z.,Zheng, X.,Xie, Q. W.,et al. A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis[J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING,2018,232(6):635-649. |
| APA | Li, D. Z.,Zheng, X.,Xie, Q. W.,&Jin, Q. B..(2018).A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis.PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING,232(6),635-649. |
| MLA | Li, D. Z.,et al."A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis".PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 232.6(2018):635-649. |
入库方式： OAI收割
来源：自动化研究所
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