A Fractional-Derivative Multi-Kernel Adaptive Learning Approach for Remaining Useful Life Prediction of Rotating Machinery.
- DOI
- 10.3390/s26134137
- Published
- 2026 Jul 1
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s26134137,
title = {A Fractional-Derivative Multi-Kernel Adaptive Learning Approach for Remaining Useful Life Prediction of Rotating Machinery.},
author = {Pan L and Xu J and Peng L and Bi D and Xie Y},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26134137},
url = {https://doi.org/10.3390/s26134137}
}RIS
TY - JOUR TI - A Fractional-Derivative Multi-Kernel Adaptive Learning Approach for Remaining Useful Life Prediction of Rotating Machinery. AU - Pan L AU - Xu J AU - Peng L AU - Bi D AU - Xie Y PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26134137 UR - https://doi.org/10.3390/s26134137 ER -
APA
L, P., J, X., L, P., D, B., & Y, X. (2026). A Fractional-Derivative Multi-Kernel Adaptive Learning Approach for Remaining Useful Life Prediction of Rotating Machinery.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26134137
Source records
- pubmed · retrieved 2026-09-27T10:41:45.512Z