Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.
- DOI
- 10.3390/s22186911
- Published
- 2022 Sep 13
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s22186911,
title = {Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.},
author = {González D and Alvarez J and Sánchez JA and Godino L and Pombo I},
year = {2022},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s22186911},
url = {https://doi.org/10.3390/s22186911}
}RIS
TY - JOUR TI - Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels. AU - González D AU - Alvarez J AU - Sánchez JA AU - Godino L AU - Pombo I PY - 2022 JO - Sensors (Basel, Switzerland) DO - 10.3390/s22186911 UR - https://doi.org/10.3390/s22186911 ER -
APA
D, G., J, A., JA, S., L, G., & I, P. (2022). Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s22186911
Source records
- pubmed · retrieved 2026-09-26T13:44:15.501Z