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 …
Fault diagnosis in rotating machines based on transfer learning: Literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
significant attention in recent years. However, traditional data-driven diagnosis approaches …
Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions
Y An, K Zhang, Y Chai, Q Liu, X Huang - Expert Systems with Applications, 2023 - Elsevier
Unsupervised domain adaptation (UDA)-based methods have made great progress in
bearing fault diagnosis under variable working conditions. However, most existing UDA …
bearing fault diagnosis under variable working conditions. However, most existing UDA …
Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research
Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …
machinery. The majority of these machines comprise rotating components and are called …
Adversarial domain-invariant generalization: A generic domain-regressive framework for bearing fault diagnosis under unseen conditions
Recently, various fault diagnosis methods based on domain adaptation (DA) have been
explored to solve the problem of discrepancy between the source and target domains …
explored to solve the problem of discrepancy between the source and target domains …
Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis
Unsupervised domain adaptation (UDA) has shown remarkable results in fault diagnosis
under changing working conditions in recent years. However, most UDA methods do not …
under changing working conditions in recent years. However, most UDA methods do not …
A partial domain adaptation scheme based on weighted adversarial nets with improved CBAM for fault diagnosis of wind turbine gearbox
Y Zhu, Y Pei, A Wang, B **e, Z Qian - Engineering Applications of Artificial …, 2023 - Elsevier
Most domain adaptation methods for fault diagnosis depend heavily on the precondition that
the source and target domain have an identical label space, which is hard to be satisfied in …
the source and target domain have an identical label space, which is hard to be satisfied in …
Signal-transformer: A robust and interpretable method for rotating machinery intelligent fault diagnosis under variable operating conditions
J Tang, G Zheng, C Wei, W Huang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As well-known, deep learning models have achieved great success in the field of intelligent
fault diagnosis. However, once the working condition changed, the diagnostic accuracy of …
fault diagnosis. However, once the working condition changed, the diagnostic accuracy of …
A systematic literature review on transfer learning for predictive maintenance in industry 4.0
The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and
digital technologies within industrial production and manufacturing systems. The objective of …
digital technologies within industrial production and manufacturing systems. The objective of …
An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment
L Chen, Y Ma, H Hu, US Khan - Measurement, 2023 - Elsevier
Fault diagnosis of bearing plays an important role in maintaining the stable operation of
rotating equipment. However, the existing approaches are not effective enough in multi …
rotating equipment. However, the existing approaches are not effective enough in multi …