摘要
A new wear localization and state identification method of mechanical transmission system is proposed based on improved Principal Component Analysis (PCA) and cluster analysis, which considers the data nonlinearity and outliers. The nonlinear and robust improvement of the PCA is realized by nonlinear mapping and fuzzy set theory. Moreover, a nonlinear data discrimination method is proposed based on principal component clustering. Finally, a case study for power shift steering transmission is conducted. The results demonstrate that the proposed method is superior to the method without considering the nonlinear data and outliers, and it can effectively improve the accuracy of wear localization and state identification.
| 投稿的翻译标题 | Wear localization and identification under nonlinear condition monitoring data |
|---|---|
| 源语言 | 繁体中文 |
| 页（从-至） | 359-365 |
| 页数 | 7 |
| 期刊 | Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) |
| 卷 | 49 |
| 期 | 2 |
| DOI | |
| 出版状态 | 已出版 - 1 3月 2019 |
关键词
- Cluster analysis
- Mechanical transmission system
- Principal components analysis
- State identification
- Vehicle engineering
- Wear location