Evolving Connectionist System and Hidden Semi-Markov Model for Learning-Based Tool Wear Monitoring and Remaining Useful Life Prediction
- DOI
- 10.1109/access.2022.3196016
- Published
- 2022
- Container
- IEEE Access
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/access.2022.3196016,
title = {Evolving Connectionist System and Hidden Semi-Markov Model for Learning-Based Tool Wear Monitoring and Remaining Useful Life Prediction},
author = {Muquan Lin and Song Wanqing and Dongdong Chen and Enrico Zio},
year = {2022},
journal = {IEEE Access},
doi = {10.1109/access.2022.3196016},
url = {https://doi.org/10.1109/access.2022.3196016}
}RIS
TY - JOUR TI - Evolving Connectionist System and Hidden Semi-Markov Model for Learning-Based Tool Wear Monitoring and Remaining Useful Life Prediction AU - Muquan Lin AU - Song Wanqing AU - Dongdong Chen AU - Enrico Zio PY - 2022 JO - IEEE Access DO - 10.1109/access.2022.3196016 UR - https://doi.org/10.1109/access.2022.3196016 ER -
APA
Lin, M., Wanqing, S., Chen, D., & Zio, E. (2022). Evolving Connectionist System and Hidden Semi-Markov Model for Learning-Based Tool Wear Monitoring and Remaining Useful Life Prediction. IEEE Access. https://doi.org/10.1109/access.2022.3196016
Source records
- crossref · retrieved 2026-11-05T04:10:16.962Z