A data-driven degradation prognostic strategy for aero-engine under various operational conditions-Reference-Cited by-同舟云学术

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A data-driven degradation prognostic strategy for aero-engine under various operational conditions

Published:2021-10 Issue: Volume:462 Page:195-207
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Wang Cunsong,Zhu Zhenghong,Lu Ningyun,Cheng Yuehua,Jiang Bin

Funder

National Natural Science Foundation of China

Nanjing University of Aeronautics and Astronautics

National Key Research and Development Program of China

Six Talent Climax Foundation of Jiangsu

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference47 articles.

1. Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey;Hanachi;IEEE Trans. Rel.,2018

2. Remaining useful life prediction for engineering systems under dynamic operational conditions: A semi-Markov decision process-based approach;Tang;Chinese J. Aeronuat.,2019

3. Islanding fault detection based on data-driven approach with active developed reactive power variation;Li;Nerocomputing,2019

4. Reliability effects of maintenance on TNEP considering preventive and corrective repairs;Mahdavi;IEEE Trans. Power Syst.,2017

5. Preventive maintenance subject to equipment unavailability;Zhu;IEEE Trans. Rel.,2019

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1. Data-driven fault detection for closed-loop T-S fuzzy systems with unknown system dynamics and its application to aero-engines;Information Sciences;2024-08

2. Time-Varying Operating Condition–Based Prediction of the Remaining Useful Life for Aeroengine;Journal of Aerospace Information Systems;2024-07

3. Channel attention & temporal attention based temporal convolutional network: A dual attention framework for remaining useful life prediction of the aircraft engines;Advanced Engineering Informatics;2024-04

4. Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network;Applied Sciences;2024-02-28

5. Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections;Reliability Engineering & System Safety;2024-02

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