Mar 29, 2023 · Prognostics, as a key enabler of CBM, involves the kernel task of estimating the remaining useful life (RUL) for engineered systems. Much ...
Remaining useful life (RUL) can be estimated based on history trajectory data, that is very important for improving maintenance schedules to avoid engineering ...
Apr 5, 2023 · Prognostics, as a key enabler of CBM, involves the kernel task of estimating the remaining useful life (RUL) for engineered systems. Much ...
Oct 22, 2024 · The estimation of Remaining Useful Life (RUL) based on historical operational data trajectories is pivotal for refining maintenance programs ...
Jun 7, 2017 · The results of this study suggest that the proposed data-driven prognostic method offers a new and promising approach. Key words: Prognostics ...
Missing: Reinforcement | Show results with:Reinforcement
A new data-driven approach with Bidirectional Long Short-Term Memory (BiLSTM) network for RUL estimation, which can make full use of the sensor date ...
Remaining Useful Life Prediction using Deep Learning Approaches
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This paper gives a brief introduction of RUL prediction and reviews the start-of-the-art DL approaches in terms of four main representative deep architectures.
Missing: Reinforcement | Show results with:Reinforcement
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In this paper, we present a deep neural network (DNN) framework for RUL prognostics that maps the monitoring data and time to a latent variable representation ...
This paper proposes a new data-driven approach for prognostics using deep convolution neural networks (DCNN).
Missing: Reinforcement | Show results with:Reinforcement
A unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework.