This study presents a new attention-based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines.

Apr 20, 2022 · A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans. Ind. Inf. 15(6), 3703–3711 ...

In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN- ...

Abstract: The entire life cycle of a turbofan engine is a type of asymmetrical process in which each engine part has different characteristics.

To further improve the prediction accuracy of LSTM networks, this paper proposes a model in which effective pre-processing steps are combined with LSTM network.

Nov 19, 2020 · This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine.

To further improve the prediction accuracy of LSTM networks, this paper proposes a model in which effective pre-processing steps are combined with LSTM network.

This paper presents a new RUL prediction method using global health degradation representation (GHDR) in federated learning (FL) framework named GHDR-FL.

Aug 18, 2024 · This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health ...

This work proposes an adaptive deep learning-based RUL prediction framework with FM recognition. First, a FM recognizer fusing physics-informed FM classifier ...