A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Z Zhu, Y Lei, G Qi, Y Chai, N Mazur, Y An, X Huang - Measurement, 2023 - Elsevier
With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing
Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …
Deep transfer learning for bearing fault diagnosis: A systematic review since 2016
The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …
training and test data follow the same distribution. This assumption, however, is not always …
Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
Recent intelligent fault diagnosis technologies can effectively identify the machinery health
condition, while they are learnt based on a closed-world assumption, ie, the training and …
condition, while they are learnt based on a closed-world assumption, ie, the training and …
Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic
H Tao, L Cheng, J Qiu… - Measurement Science and …, 2022 - iopscience.iop.org
With the rapid development of industrial informatization and deep learning technology,
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …
modern data-driven fault diagnosis (MIFD) methods based on deep learning have been …
Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions
X Yan, D She, Y Xu - Expert Systems with Applications, 2023 - Elsevier
Because of the complex operating environment of high-end industrial machinery, rolling
bearing is generally operated at fluctuating working conditions such as variable speeds or …
bearing is generally operated at fluctuating working conditions such as variable speeds or …
Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …
years; however, most of these methods are coarse grained and data demanding that cannot …
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …
tremendous progress, which can help reduce costly breakdowns. However, different …
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 …