Name
Title | ||
|---|---|---|
Life prediction of CNC linear rolling guide based on DFNN performance degradation model |
Abstract | ||
|---|---|---|
In order to improve the performance of linear rolling guide on different machining condition, a guide performance degradation model based on dynamic fuzzy neural network (DFNN) was proposed to predict guide life after considering a variety of factors to guide's motion precision and wear status in this paper. The vibration signals from linear rolling guide were processed by time domain analysis and the wavelet packet transformation, the most sensitive features to guide's performance were selected as the input vector of model by analyzing changing trend, the nonlinear relation between features and guide life was built by DFNN which parameters were acquired by on-line training, the actual residual life of linear rolling guide was gotten by comparing with the rated life under different machining conditions. The experimental results show that the model can accurately predict the dynamic life of the guide of the CNC machine tool feed drive system effectively. It is conducive to the maintenance of guide. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/FSKD.2010.5569106 | FSKD |
Keywords | Field | DocType |
dynamic fuzzy neural network,performance degradation,online training,mechanical guides,dfnn,machine tool feed drive,vibration signal,actual residual life,production engineering computing,life prediction,wavelet transforms,time domain analysis,vibrations,wavelet transformation,machine tools,linear rolling guide,time-domain analysis,performance degradation model,cnc linear rolling guide,computerised numerical control,motion precision,remaining life assessment,fuzzy neural nets,degradation,fuzzy neural network,mathematical model,wavelet packet transform,computer numerical control,machine tool,wavelet packets,wavelet analysis | Nonlinear system,Numerical control,Computer science,Control engineering,Artificial intelligence,Artificial neural network,Wavelet packet decomposition,Wavelet transform,Machine tool,Residual,Simulation,Machining,Machine learning | Conference |
Volume | ISBN | Citations |
3 | 978-1-4244-5931-5 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baiquan Huang | 1 | 0 | 0.34 |
| Hongli Gao | 2 | 4 | 9.32 |
| Mingheng Xu | 3 | 1 | 1.79 |
| Xixi Wu | 4 | 0 | 0.34 |
| Min Zhao | 5 | 0 | 0.34 |
| Liang Guo | 6 | 2 | 5.10 |