Abstract
Here, to improve the prediction accuracy of tool wear state, a tool residual life prediction method based on multi-sensor fusion was proposed. In training phase, firstly, combining vibration, current and PLC controller information, data were preprocessed, and the time series analysis method was used to do feature extraction; then, aiming at the problem of single-frame sample lacking context information and being not able to cover the whole life cycle data, multi-frame combination and the mix-up method were used to enhance data; finally, a deep neural network was designed to learn complex nonlinear functions among multi-modal input features and tool residual life. In test phase, median filtering was used to remove effects of noise and obtain the final predicted value. The experimental results showed that the effectiveness of multi-sensor fusion is verified; using multi-modal data and introducing data enhancement can significantly improve the prediction accuracy of tool wear.
Translated title of the contribution | Tool residual life prediction based on multi-sensor fusion |
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Original language | Chinese (Simplified) |
Pages (from-to) | 47-54 |
Number of pages | 8 |
Journal | Zhendong yu Chongji/Journal of Vibration and Shock |
Volume | 40 |
Issue number | 17 |
DOIs | |
Publication status | Published - Sept 15 2021 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
ASJC Scopus Subject Areas
- Acoustics and Ultrasonics
- Mechanics of Materials
- Mechanical Engineering
Keywords
- Data enhancement
- Deep neural network
- Multi-sensor information fusion
- Residual life prediction
- Tool condition monitoring