Name
Abstract | ||
|---|---|---|
In this paper, the tooth-shaped tool in CNC machine tools is taken as the research object, and the time series of tool vibration signal is estimated based on the deep learning algorithm LSTM, which verifies the effectiveness of using LSTM to predict the vibration signal. Firstly, the experimental system obtains the time series of tool vibration signals, and denoises the obtained experimental data by wavelet, and analyzes the frequency distribution diagram of the system. On this basis, SE complexity analysis of vibration signal shows that the greater the wear value, the greater the complexity of vibration signal. According to the results of wavelet transform, LSTM algorithm is adopted, and the accuracy rate reaches 99%, which provides a new idea for intelligent equipment operation and maintenance technology. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/BigDataSecurityHPSCIDS54978.2022.00033 | 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
Keywords | DocType | ISBN |
CNC,LSTM,Wavelet transformation,Prediction | Conference | 978-1-6654-8070-3 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Tengfei | 1 | 1 | 0.69 |
| Fu Haiyan | 2 | 1 | 0.69 |
| Huang Lili | 3 | 0 | 0.34 |
| Zang Hongyan | 4 | 0 | 0.34 |
| Zhou You | 5 | 0 | 0.34 |
| Yan Tingyang | 6 | 1 | 0.69 |