Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.
- DOI
- 10.1016/j.neunet.2020.07.001
- Published
- 2020 Oct
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2020.07.001,
title = {Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.},
author = {Shen Y and Khorasani K},
year = {2020},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2020.07.001},
url = {https://doi.org/10.1016/j.neunet.2020.07.001}
}RIS
TY - JOUR TI - Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. AU - Shen Y AU - Khorasani K PY - 2020 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2020.07.001 UR - https://doi.org/10.1016/j.neunet.2020.07.001 ER -
APA
Y, S., & K, K. (2020). Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2020.07.001
Source records
- pubmed · retrieved 2026-09-26T10:34:08.553Z