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 …
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 …
Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network
Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is
based on single-source domain adaptation, which fails to simultaneously utilize various …
based on single-source domain adaptation, which fails to simultaneously utilize various …
Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain
Unsupervised cross-domain fault diagnosis of bearings has practical significance; however,
the existing studies still face some problems. For example, transfer diagnosis scenarios are …
the existing studies still face some problems. For example, transfer diagnosis scenarios are …
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 …
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains
Transfer learning based on a single source domain to a target domain has received a lot of
attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical …
attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical …
Domain adversarial graph convolutional network for fault diagnosis under variable working conditions
Unsupervised domain adaptation (UDA)-based methods have made great progress in
mechanical fault diagnosis under variable working conditions. In UDA, three types of …
mechanical fault diagnosis under variable working conditions. In UDA, three types of …
Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives
Intelligent fault diagnosis has been a promising way for condition-based maintenance.
However, the small sample problem has limited the application of intelligent fault diagnosis …
However, the small sample problem has limited the application of intelligent fault diagnosis …
Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study
Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …
representation learning and plenty of labeled data. However, machines often operate with …
Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching
S Liu, H Jiang, Z Wu, Z Yi, R Wang - Reliability Engineering & System …, 2023 - Elsevier
In the health management of modern rotating machinery, domain adaptation is an effective
method to solve the diagnostic problems of insufficient labeled signals and poor …
method to solve the diagnostic problems of insufficient labeled signals and poor …