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 …
Intelligent diagnosis using continuous wavelet transform and gauss convolutional deep belief network
H Zhao, J Liu, H Chen, J Chen, Y Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Bearing fault diagnosis is of significance to ensure the safe and reliable operation of a
motor. Deep learning provides a powerful ability to extract the features of raw data …
motor. Deep learning provides a powerful ability to extract the features of raw data …
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
Applications of machine learning to machine fault diagnosis: A review and roadmap
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …
machine fault diagnosis. This is a promising way to release the contribution from human …
[HTML][HTML] A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges …
M Hakim, AAB Omran, AN Ahmed, M Al-Waily… - Ain Shams Engineering …, 2023 - Elsevier
Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …
[HTML][HTML] Potential, challenges and future directions for deep learning in prognostics and health management applications
Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …
domains, including computer vision and natural language understanding. The drivers for the …
Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning
Y Li, Y Song, L Jia, S Gao, Q Li… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart
manufacturing. The massive amount of data in IIoT promote the development of deep …
manufacturing. The massive amount of data in IIoT promote the development of deep …
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …
popularly developed in the past years. While promising diagnostic performance has been …
A comprehensive review on convolutional neural network in machine fault diagnosis
With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …
increasingly significant to ensure safe equipment operation and production. Consequently …