Residual Life Prediction of Rolling Bearings Driven by Digital Twins DOI Open Access

Jiayi Fan,

Lijuan Zhao, Minghao Li

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 406 - 406

Published: March 8, 2025

To enhance the maintenance efficiency and operational stability of rolling bearings, this work establishes a methodology for bearing life prediction, employing digital twin systems to evaluate remaining useful bearings. A comprehensive twin-integrated model entire lifecycle bearings is constructed using Modelica language. This generates sufficient reliable data Due symmetrical physical structure generated also have symmetry. Based on characteristic (RUL) prediction algorithm developed recurrent neural network (RNN), specifically an improved gated unit (GRU) model. An optimization employed adjust hyperparameters determine initial fault point bearing. multi-feature dataset constructed, effectively enhancing precision reliability lifespan estimation. existing measured bearing’s lifecycle, parameters are updated. Through parameter degradation component twin, generated. By combining with actual measurement data, method addresses limitations traditional approaches in situations where complete scarce, providing technical support intelligent

Language: Английский

Sparse Graph Structure Fusion Convolutional Network for Machinery Remaining Useful Life Prediction DOI
Lingli Cui, Qiang Shen, Yongchang Xiao

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: unknown, P. 110592 - 110592

Published: Oct. 1, 2024

Language: Английский

Citations

4
A two-stage remaining useful life prediction method based on adaptive feature metric and graph spatiotemporal attention rule learning DOI
Shaoyang Liu,

Jingfeng Wei,

Guofa Li

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110802 - 110802

Published: Jan. 1, 2025

Language: Английский

Citations

0
Dynamic Model-driven Dictionary Learning-inspired Domain Adaptation Strategy for Cross-domain Bearing Fault Diagnosis DOI
Zhengyu Du, Dongdong Liu, Lingli Cui

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110905 - 110905

Published: Feb. 1, 2025

Language: Английский

Citations

0
Enhanced deep learning framework for accurate near-failure RUL prediction of bearings in varying operating conditions DOI
Anil Kumar,

Chander Parkash,

Pradeep Kundu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103231 - 103231

Published: March 1, 2025

Language: Английский

Citations

0
Residual Life Prediction of Rolling Bearings Driven by Digital Twins DOI Open Access

Jiayi Fan,

Lijuan Zhao, Minghao Li

et al.

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 406 - 406

Published: March 8, 2025

To enhance the maintenance efficiency and operational stability of rolling bearings, this work establishes a methodology for bearing life prediction, employing digital twin systems to evaluate remaining useful bearings. A comprehensive twin-integrated model entire lifecycle bearings is constructed using Modelica language. This generates sufficient reliable data Due symmetrical physical structure generated also have symmetry. Based on characteristic (RUL) prediction algorithm developed recurrent neural network (RNN), specifically an improved gated unit (GRU) model. An optimization employed adjust hyperparameters determine initial fault point bearing. multi-feature dataset constructed, effectively enhancing precision reliability lifespan estimation. existing measured bearing’s lifecycle, parameters are updated. Through parameter degradation component twin, generated. By combining with actual measurement data, method addresses limitations traditional approaches in situations where complete scarce, providing technical support intelligent

Language: Английский

Citations

0