Navigating the Prognostics Landscape: Deep Reinforcement Learning-Enabled Remaining Useful Life Estimation with Novel Methodology-Reference-Cited by-同舟云学术

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Navigating the Prognostics Landscape: Deep Reinforcement Learning-Enabled Remaining Useful Life Estimation with Novel Methodology

Published:2024-05-09 Issue: Volume:2 Page:520-526
ISSN:
Container-title:2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)
language:
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Author:

Kashyap Ramgopal¹,Al Zainab Failh²,Naser Zamen Latef³,Abdulhasan Maki Mahdi⁴

Affiliation:

1. Amity University,Raipur,Chhattisgarh,India

2. Al-Manara College for Medical Sciences,Maysan,Iraq

3. College of Computer, National University of Science and Technology,Dhi Qar,Iraq

4. AL-Nisour University College,Department of Medical Laboratories Technology,Baghdad,Iraq

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10592860/10592845/10592974.pdf?arnumber=10592974

Reference24 articles.

1. Machinery health prognostics: A systematic review from data acquisition to RUL prediction

2. Deep learning for prognostics and health management: State of the art, challenges, and opportunities

3. A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

4. Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

5. Stochastic Dilated Residual Ghost Model for Breast Cancer Detection

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