[HTML][HTML] A survey on machine learning based analysis of heterogeneous data in industrial automation
In many application domains data from different sources are increasingly available to
thoroughly monitor and describe a system or device. Especially within the industrial …
thoroughly monitor and describe a system or device. Especially within the industrial …
Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions
Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
Adversarial deep transfer learning in fault diagnosis: progress, challenges, and future prospects
Y Guo, J Zhang, B Sun, Y Wang - Sensors, 2023 - mdpi.com
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …
superiorities of deep learning in feature representation with the merits of transfer learning in …
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 …
Intelligent fault diagnosis for bearings of industrial robot joints under varying working conditions based on deep adversarial domain adaptation
Industrial robots are one of the most typical machines in smart manufacturing systems. Their
joint bearing faults account for a significant portion of failures. Data-driven bearing fault …
joint bearing faults account for a significant portion of failures. Data-driven bearing fault …
A meta-learning network with anti-interference for few-shot fault diagnosis
Z Zhao, R Zhao, X Wu, X Hu, R Che, X Zhang, Y Jiao - Neurocomputing, 2023 - Elsevier
Considering the changing working conditions of rotating machinery in operation, it is often
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …
difficult to collect data accurately in some severe fault states, and the lack of data can lead to …
Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery
Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical
significance, which still exists some challenges, for example, the learned domain-specific …
significance, which still exists some challenges, for example, the learned domain-specific …
Federated domain generalization with global robust model aggregation strategy for bearing fault diagnosis
X Cong, Y Song, Y Li, L Jia - Measurement Science and …, 2023 - iopscience.iop.org
Federated learning ensures the privacy of fault diagnosis by maintaining a decentralized
and local training data approach, eliminating the need to share confidential information with …
and local training data approach, eliminating the need to share confidential information with …
A progressive multi-source domain adaptation method for bearing fault diagnosis
Based on massive samples collected from various working conditions, multi-source domain
adaptation-based fault diagnosis methods have been a promising way to improve the …
adaptation-based fault diagnosis methods have been a promising way to improve the …
[HTML][HTML] A review of maritime equipment prognostics health management from a classification society perspective
With the development of digital technology, the maritime industry is under continuous digital
transformation. For example, from manned engine room to control room and even to …
transformation. For example, from manned engine room to control room and even to …