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 …
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 …
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 …
Deep discriminative transfer learning network for cross-machine fault diagnosis
Many domain adaptation methods have been presented to deal with the distribution
alignment and knowledge transfer between the target domain and the source domain …
alignment and knowledge transfer between the target domain and the source domain …
Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis
Discrepancy representation metric completely determines the transfer diagnosis
performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) …
performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) …
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 …
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 …
Thermographic fault diagnosis of shaft of BLDC motor
A Glowacz - Sensors, 2022 - mdpi.com
A technique of thermographic fault diagnosis of the shaft of a BLDC (Brushless Direct
Current Electric) motor is presented in this article. The technique works for the shivering of …
Current Electric) motor is presented in this article. The technique works for the shivering of …
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 …