Name
Abstract | ||
|---|---|---|
Compared with the traditional bearing fault diagnosis methods, the convolution neural network (CNN) can automatically extract features. However, the construction of CNN model usually needs a large dataset, and it is very timeconsuming to train a CNN model. To address this issue, a feature transferring fault diagnosis method is proposed. Firstly, raw signals are decomposed into sub-signals of different frequencies by wavelet packet decomposition, and the subsignals are refactored into a new signal in order to filter noise. Secondly, 2D time-frequency images are constructed by the frequency slice wavelet transform to enhance signal feature. Finally, the proposed model is trained to identify classification. The effectiveness of proposed method is verified on the famous motor bearing data provided by the Case Western Reserve University. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/I2MTC43012.2020.9129483 | 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) |
Keywords | DocType | ISBN |
Fault diagnosis,GoogLeNet,Feature transferring,Signal preprocessing and conversion | Conference | 978-1-7281-4460-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guannan Cao | 1 | 0 | 0.34 |
| Kaifeng Zhang | 2 | 0 | 0.34 |
| Kaibo Zhou | 3 | 0 | 0.34 |
| Hao Pan | 4 | 46 | 6.94 |
| Yanhe Xu | 5 | 18 | 5.39 |
| Jie Liu | 6 | 0 | 0.34 |