Cargando…
Remaining Useful Life Prediction Using Dual-Channel LSTM with Time Feature and Its Difference
At present, the research on the prediction of the remaining useful life (RUL) of machinery mainly focuses on multi-sensor feature extraction and then uses the features to predict RUL. In complex operations and multiple abnormal environments, the impact of noise may result in increased model complexi...
Autores principales: | Peng, Cheng, Wu, Jiaqi, Wang, Qilong, Gui, Weihua, Tang, Zhaohui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778194/ https://www.ncbi.nlm.nih.gov/pubmed/36554221 http://dx.doi.org/10.3390/e24121818 |
Ejemplares similares
-
Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion
por: Peng, Cheng, et al.
Publicado: (2022) -
A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion
por: Peng, Cheng, et al.
Publicado: (2021) -
A Multi-Featured Factor Analysis and Dynamic Window Rectification Method for Remaining Useful Life Prognosis of Rolling Bearings
por: Peng, Cheng, et al.
Publicado: (2023) -
Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification
por: Yang, Jinsong, et al.
Publicado: (2022) -
A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction
por: Zhao, Chengying, et al.
Publicado: (2020)