[HTML][HTML] Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
In recent years, research has proposed several deep learning (DL) approaches to providing
reliable remaining useful life (RUL) predictions in Prognostics and Health Management …
reliable remaining useful life (RUL) predictions in Prognostics and Health Management …
Remaining useful life prediction of turbofan engine using global health degradation representation in federated learning
In recent years, deep neural networks have been widely applied in remaining useful life
(RUL) prediction, and good prognostic performance has been achieved. However, existing …
(RUL) prediction, and good prognostic performance has been achieved. However, existing …
A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction
Deep learning (DL)-based methods for remaining useful life (RUL) prediction have received
increasing research attention due to excellent feature extraction abilities. Most DL methods …
increasing research attention due to excellent feature extraction abilities. Most DL methods …
Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes
Data-driven remaining useful life (RUL) prediction approaches, especially those based on
deep learning (DL), have been increasingly applied to mechanical equipment. However, two …
deep learning (DL), have been increasingly applied to mechanical equipment. However, two …
Deep-learning based prognosis approach for remaining useful life prediction of turbofan engine
The entire life cycle of a turbofan engine is a type of asymmetrical process in which each
engine part has different characteristics. Extracting and modeling the engine symmetry …
engine part has different characteristics. Extracting and modeling the engine symmetry …
Remaining useful life prediction of turbofan engines using CNN-LSTM-SAM approach
J Li, Y Jia, M Niu, W Zhu, F Meng - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Accurate remaining useful life (RUL) prediction of turbofan engines can effectively avoid
serious air disaster due to engine failure by mining its component degradation …
serious air disaster due to engine failure by mining its component degradation …
Remaining useful life estimation using deep convolutional generative adversarial networks based on an autoencoder scheme
G Hou, S Xu, N Zhou, L Yang… - Computational Intelligence …, 2020 - Wiley Online Library
Accurate predictions of remaining useful life (RUL) of important components play a crucial
role in system reliability, which is the basis of prognostics and health management (PHM) …
role in system reliability, which is the basis of prognostics and health management (PHM) …
A deep learning model for remaining useful life prediction of aircraft turbofan engine on C-MAPSS dataset
In the era of industry 4.0, safety, efficiency and reliability of industrial machinery is an
elementary concern in trade sectors. The accurate remaining useful life (RUL) prediction of …
elementary concern in trade sectors. The accurate remaining useful life (RUL) prediction of …
Data-driven deep learning-based attention mechanism for remaining useful life prediction: Case study application to turbofan engine analysis
Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great
significance for improving the reliability and safety of the engine system. Due to the high …
significance for improving the reliability and safety of the engine system. Due to the high …
Fusing physics-based and deep learning models for prognostics
Physics-based and data-driven models for remaining useful lifetime (RUL) prediction
typically suffer from two major challenges that limit their applicability to complex real-world …
typically suffer from two major challenges that limit their applicability to complex real-world …
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