Fault knowledge transfer assisted ensemble method for remaining useful life prediction
Machinery remaining useful life (RUL) prediction is an important task in condition-based
maintenance. Data-driven methods have been widely studied and applied, however, almost …
maintenance. Data-driven methods have been widely studied and applied, however, almost …
A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction
Deep learning (DL)-based methods for remaining useful life (RUL) prediction have received
increasing research attention due to excellent feature extraction abilities. Most DL methods …
increasing research attention due to excellent feature extraction abilities. Most DL methods …
Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data
H Cheng, X Kong, Q Wang, H Ma, S Yang… - Reliability Engineering & …, 2023 - Elsevier
The remaining useful life (RUL) prediction is critically involved in machinery to ensure safe
and reliable operation. Nevertheless, acquiring the full-cycle degradation data is difficult and …
and reliable operation. Nevertheless, acquiring the full-cycle degradation data is difficult and …
Long short-term memory neural network with transfer learning and ensemble learning for remaining useful life prediction
L Wang, H Liu, Z Pan, D Fan, C Zhou, Z Wang - Sensors, 2022 - mdpi.com
Prediction of remaining useful life (RUL) is greatly significant for improving the safety and
reliability of manufacturing equipment. However, in real industry, it is difficult for RUL …
reliability of manufacturing equipment. However, in real industry, it is difficult for RUL …
A novel combination neural network based on convlstm-transformer for bearing remaining useful life prediction
A sensible maintenance strategy must take into account the remaining usable life (RUL)
estimation to maximize equipment utilization and avoid costly unexpected breakdowns. In …
estimation to maximize equipment utilization and avoid costly unexpected breakdowns. In …
A selective adversarial adaptation network for remaining useful life prediction of machines under different working conditions
Deep neural networks have been widely applied in machinery health prognostics due to
their powerful feature learning capacity. However, in many existing remaining useful life …
their powerful feature learning capacity. However, in many existing remaining useful life …
Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions
H Cheng, X Kong, Q Wang, H Ma, S Yang… - Journal of Intelligent …, 2023 - Springer
Remaining useful life (RUL) prediction can effectively avoid unexpected mechanical
breakdowns, thus improving operational reliability. However, the distribution discrepancy …
breakdowns, thus improving operational reliability. However, the distribution discrepancy …
Simultaneous bearing fault recognition and remaining useful life prediction using joint-loss convolutional neural network
Fault diagnosis and remaining useful life (RUL) prediction are always two major issues in
modern industrial systems, which are usually regarded as two separated tasks to make the …
modern industrial systems, which are usually regarded as two separated tasks to make the …
A deep adversarial learning prognostics model for remaining useful life prediction of rolling bearing
Remaining useful life (RUL) prediction for condition-based maintenance decision making
plays a key role in prognostics and health management (PHM). Accurately predicting RUL of …
plays a key role in prognostics and health management (PHM). Accurately predicting RUL of …
Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation
HB Zhang, DJ Cheng, KL Zhou, SW Zhang - Knowledge-Based Systems, 2023 - Elsevier
The multi-stage degradation process of bearings significantly affects the predicted
performance of rotating machinery and equipment in long-term operation. However, the …
performance of rotating machinery and equipment in long-term operation. However, the …
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