Name
Abstract | ||
|---|---|---|
Intelligent manufacturing poses a challenge for fault diagnosis of rotor systems to meet the three tasks: whether exists faults, faults location and quantitative diagnosis. Traditional methods hardly meet all the three tasks in online fault diagnosis. This paper proposes a modified classification and regression tree (CART) algorithm named D-CART algorithm to provide much faster fault classification by reducing the iteration times in computation while still ensuring accuracy. Experiments are carried on to achieve a comprehensive online fault diagnosis for rotor systems such as faults location, faults types and quantitative analysis of unbalanced mass in this paper. In comparison with the other 4 novel CART-based algorithms, the experimental results indicate that the speed of D-CART algorithm is improved by a factor of 23.92 compared to the fastest improved algorithm (Adaboost-CART) and a model accuracy of up to 96.77%. Thus demonstrating the speed superiority of D-CART algorithm in both diagnosing locations of different faults types and determining the loading masses of unbalanced faults. The proposed method has the potential to realize high-accuracy online fault diagnosis for rotor systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10489-019-01516-2 | Applied Intelligence |
Keywords | Field | DocType |
Data mining, CART, Fault diagnosis, Intelligent manufacturing | Decision tree,Computer science,Cart,Algorithm,Rotor (electric),Artificial intelligence,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
50 | 1 | 0924-669X |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaxia Deng | 1 | 4 | 2.17 |
| Yifan Diao | 2 | 1 | 0.35 |
| Wei Wu | 3 | 1 | 1.03 |
| Jin Zhang | 4 | 4 | 2.17 |
| Mengchao Ma | 5 | 5 | 2.60 |
| Xiang Zhong | 6 | 1 | 0.35 |