Imagen de Portada

Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xu, Zhaoyi, Guo, Yanjie, Saleh, Joseph Homer
Formato:	Preprint
Publicado:	2021
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Hybrid Deep Learning with Temporal Data Augmentation for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries
por: Tian, Yun, et al.
Publicado: (2026)

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

Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
por: Belaunzaran, Xabier, et al.
Publicado: (2026)

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

SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction
por: Fan, Junhao, et al.
Publicado: (2025)

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

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

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

Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation
por: Cohen, Joseph, et al.
Publicado: (2023)

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
por: Della Libera, Luca, et al.
Publicado: (2024)

Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction
por: Mohajerzarrinkelk, Ali, et al.
Publicado: (2025)

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

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

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

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

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

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

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

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

Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
por: Gu, Minghao, et al.
Publicado: (2026)

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

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
por: James, Zachary, et al.
Publicado: (2025)

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

Rethinking Remaining Useful Life Prediction with Scarce Time Series Data: Regression under Indirect Supervision
por: Cheng, Jiaxiang, et al.
Publicado: (2025)

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

Regular Fourier Features for Nonstationary Gaussian Processes
por: Jawaid, Arsalan, et al.
Publicado: (2026)

Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing
por: Tao, Laifa, et al.
Publicado: (2025)

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

PerFormer: A Permutation Based Vision Transformer for Remaining Useful Life Prediction
por: Fan, Zhengyang, et al.
Publicado: (2025)

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

Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction
por: Barry-Straume, Jostein, et al.
Publicado: (2026)

Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction
por: Jiang, Feilong, et al.
Publicado: (2024)

A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
por: Wang, Yucheng, et al.
Publicado: (2024)

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

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

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

Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes
por: Rani, Anju, et al.
Publicado: (2026)

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

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
por: Negarandeh, Nima, et al.
Publicado: (2025)

Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
por: Alvi, Sk Md Ahnaf Akif, et al.
Publicado: (2025)