Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …
challenges such as complex degradation processes, varying working conditions, and …
[HTML][HTML] Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods
C Ferreira, G Gonçalves - Journal of Manufacturing Systems, 2022 - Elsevier
Abstract Approaches such as Cyber-Physical Systems (CPS), Internet of Things (IoT),
Internet of Services (IoS), and Data Analytics have built a new paradigm called Industry 4.0 …
Internet of Services (IoS), and Data Analytics have built a new paradigm called Industry 4.0 …
Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks
In recent years, intelligent data-driven prognostic methods have been successfully
developed, and good machinery health assessment performance has been achieved …
developed, and good machinery health assessment performance has been achieved …
Intelligent machinery fault diagnosis with event-based camera
Event-based cameras are the emerging bioinspired technology in vision sensing. Different
from the traditional standard cameras, the event-based cameras asynchronously record the …
from the traditional standard cameras, the event-based cameras asynchronously record the …
A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition
Most supervised learning-based approaches follow the assumptions that offline data and
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
online data must obey a similar distribution, which is difficult to satisfy in realistic remaining …
Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis
W Zhang, Z Wang, X Li - Reliability Engineering & System Safety, 2023 - Elsevier
Due to the limitations of data quality and quantity of a single industrial user, the development
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …
A survey of transfer learning for machinery diagnostics and prognostics
In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …
components greatly influence operational safety and system reliability. Many data-driven …
Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data
Most existing deep learning (DL)-based health prognostic methods assume that the training
and testing datasets are from identical machines operating under similar conditions …
and testing datasets are from identical machines operating under similar conditions …
[HTML][HTML] An AIS-based deep learning framework for regional ship behavior prediction
This study presents a deep learning framework to support regional ship behavior prediction
using historical AIS data. The framework is meant to aid in proactive collision avoidance, in …
using historical AIS data. The framework is meant to aid in proactive collision avoidance, in …
Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction
Deep learning-based prognostic methods have achieved great success in remaining useful
life (RUL) prediction, since degradation information of machine can be adequately mined by …
life (RUL) prediction, since degradation information of machine can be adequately mined by …