Buchumschlag

A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Wang, Yucheng, Wu, Min, Li, Xiaoli, Xie, Lihua, Chen, Zhenghua
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
von: Wang, Yucheng, et al.
Veröffentlicht: (2025)

Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation
von: Hou, Yubo, et al.
Veröffentlicht: (2026)

Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
von: Wen, Zhihao, et al.
Veröffentlicht: (2024)

Automated Machine Learning for Remaining Useful Life Predictions
von: Zöller, Marc-André, et al.
Veröffentlicht: (2023)

Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Prediction
von: Hou, Yubo, et al.
Veröffentlicht: (2025)

Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction
von: Wang, Yu, et al.
Veröffentlicht: (2025)

Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
von: Furqon, Muhammad Tanzil, et al.
Veröffentlicht: (2024)

BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction
von: Zhang, Zekai, et al.
Veröffentlicht: (2025)

IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction
von: Ye, Guangzai, et al.
Veröffentlicht: (2024)

Multiscale Dual-path Feature Aggregation Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
von: Lv, Zihao, et al.
Veröffentlicht: (2025)

A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions
von: Arunan, Anushiya, et al.
Veröffentlicht: (2024)

Supervised Contrastive Learning based Dual-Mixer Model for Remaining Useful Life Prediction
von: Fu, En, et al.
Veröffentlicht: (2024)

HyBattNet: Hybrid Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries
von: Tran, Khoa, et al.
Veröffentlicht: (2025)

A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN)
von: Imbert, Florent, et al.
Veröffentlicht: (2026)

A Multi-Granularity Supervised Contrastive Framework for Remaining Useful Life Prediction of Aero-engines
von: He, Zixuan, et al.
Veröffentlicht: (2024)

Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning
von: Wang, Yucheng, et al.
Veröffentlicht: (2024)

Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life
von: Xue, Jingyuan, et al.
Veröffentlicht: (2025)

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation
von: G, Muthukumar, et al.
Veröffentlicht: (2024)

Uncertainty-Aware Deep Learning Framework for Remaining Useful Life Prediction in Turbofan Engines with Learned Aleatoric Uncertainty
von: Sharma, Krishang
Veröffentlicht: (2025)

A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Models
von: Frizzo, Davide, et al.
Veröffentlicht: (2025)

FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Prediction
von: Chirukiri, Varun Teja, et al.
Veröffentlicht: (2025)

From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
von: Geng, Xue, et al.
Veröffentlicht: (2024)

Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making
von: Paneru, Biplov, et al.
Veröffentlicht: (2024)

A Novel Multimodal RUL Framework for Remaining Useful Life Estimation with Layer-wise Explanations
von: Razzaq, Waleed, et al.
Veröffentlicht: (2025)

Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation
von: Gong, Peiliang, et al.
Veröffentlicht: (2025)

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
von: Niu, Tian, et al.
Veröffentlicht: (2024)

Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning
von: Jeong, Cheoljoon, et al.
Veröffentlicht: (2025)

Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models
von: Chen, Yan, et al.
Veröffentlicht: (2024)

Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction
von: Huang, Zhixin, et al.
Veröffentlicht: (2024)

A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life
von: Wang, Chenhan, et al.
Veröffentlicht: (2026)

Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization
von: Gong, Peiliang, et al.
Veröffentlicht: (2025)

Turbofan Engine Remaining Useful Life (RUL) Prediction Based on Bi-Directional Long Short-Term Memory (BLSTM)
von: Sherifi, Abedin
Veröffentlicht: (2024)

A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction
von: Liu, Zhipeng, et al.
Veröffentlicht: (2025)

Graph-Aware Contrasting for Multivariate Time-Series Classification
von: Wang, Yucheng, et al.
Veröffentlicht: (2023)

Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
von: Wang, Yucheng, et al.
Veröffentlicht: (2023)

Trustworthy Graph Neural Networks: Aspects, Methods and Trends
von: Zhang, He, et al.
Veröffentlicht: (2022)

PEFT-MuTS: A Multivariate Parameter-Efficient Fine-Tuning Framework for Remaining Useful Life Prediction based on Cross-domain Time Series Representation Model
von: Fu, En, et al.
Veröffentlicht: (2026)

Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
von: Yang, Xiao, et al.
Veröffentlicht: (2024)

Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
von: Gong, Peiliang, et al.
Veröffentlicht: (2025)

Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks
von: Wu, Yucheng, et al.
Veröffentlicht: (2024)