A Semisupervised Deep Hybrid Multitask Model for RUL Prediction-Reference-Cited by-同舟云学术

A Semisupervised Deep Hybrid Multitask Model for RUL Prediction

Published:2023 Issue: Volume:72 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Lin Yan-Hui¹^ORCID,Guan Lu-Xin¹^ORCID,Chang Liang¹^ORCID,Zio Enrico²^ORCID

Affiliation:

1. School of Reliability and Systems Engineering and the Science and Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing, China

2. Energy Department, Politecnico di Milano, Milan, Italy

Funder

Stable Supporting Fund of Science and Technology on Reliability and Environmental Engineering Laboratory

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10172257.pdf?arnumber=10172257

Reference41 articles.

1. Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series

2. Outlier Detection for Multidimensional Time Series Using Deep Neural Networks

3. Unsupervised health Indicator construction by a novel degradation-trend-constrained variational autoencoder and its applications

4. Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder

5. Semi-supervised learning with temporal variational auto-encoders for reliability;silva;Proc 31st Eur Saf Rel Conf (ESREL),2021

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