Name
Abstract | ||
|---|---|---|
The rolling bearing fault diagnosis with vibration data is critical to the reliability and the safety of rotating machinery. According to the non-stationary characteristics and the simple logical structure characteristics of rolling bearing vibration data, a rolling bearing fault diagnosis method based on modified bidirectional long short-term memory (Bi-LSTM) neural network is put forward in this paper. Firstly, original vibration data are decomposed into time-frequency feature with the combination of Daubechies 10 wavelet packet transform and Symlets 8 wavelet packet transform. Then, we design bidirectional long-term memory (Bi-LTM) neural network, the Bi-LTM neural network only uses long-term memory to process rolling bearing feature data and get the result of fault diagnosis. In order to enhance functionality of the Bi-LTM internal activation function, the Bi-LTM internal function uses softsign. We evaluate our models on a standard dataset. Moreover, given the analytical results, compared to Bi-LSTM, the proposed Bi-LTM method further reduces the rolling bearing fault diagnosis error rate by 6 times. Numerical and simulation results verify that the rolling bearing fault diagnosis method based on the proposed method is justified. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICC.2019.8761383 | IEEE International Conference on Communications |
Keywords | Field | DocType |
Rolling bearing fault diagnosis,Bidirectional long-term memory (Bi-LTM) neural network,Double wavelet packet transform | Computer science,Activation function,Word error rate,Algorithm,Bearing (mechanical),Real-time computing,Vibration,Artificial neural network,Wavelet packet decomposition,Feature data | Conference |
ISSN | Citations | PageRank |
1550-3607 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dawei Qiu | 1 | 13 | 3.09 |
| Zichen Liu | 2 | 1 | 1.02 |
| Yiqing Zhou | 3 | 973 | 75.42 |
| Jinglin Shi | 4 | 571 | 59.91 |