In the two-stage framework-based RUL prediction approach, firstly, the first predicting time (FPT) identification is used to judge the start time of the mechanical degradation state [6]. Once the degradation state is detected, the RUL prediction is triggered to obtain the RUL of the mechanical equipment [7,8].
In order to conquer this problem, a two-stage RUL prediction method is proposed for the cross-domain prognostic task with insufficient degradation data. At ...
A novel two-stage method via adversarial strategy for remaining useful life prediction of bearings under variable conditions · Multi-feature spaces cross ...
The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data. Article. May 2022; RELIAB ENG SYST ...
"The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and ...
A unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework.
Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure.
Based on TMSCNN, a two-stage transfer learning framework is introduced to address the challenge of training deep learning models of the RUL prediction task with ...
Feb 19, 2024 · This paper focuses on predicting the RUL of aircraft engines with limited data. We elaborate on the related work of RUL prediction from two ...
Cheng, The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data, Reliab Eng Syst Saf, № 225