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 …
GAN-based anomaly detection: A review
X Xia, X Pan, N Li, X He, L Ma, X Zhang, N Ding - Neurocomputing, 2022 - Elsevier
Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …
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 …
Disrupting 3D printing of medicines with machine learning
3D printing (3DP) is a progressive technology capable of transforming pharmaceutical
development. However, despite its promising advantages, its transition into clinical settings …
development. However, despite its promising advantages, its transition into clinical settings …
Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks
M Zhao, X Fu, Y Zhang, L Meng, B Tang - Advanced Engineering …, 2022 - Elsevier
The healthy operations of mechanical systems are crucially important for ensuring human
safety and economic benefits, so that there is a high demand on the automatic fault …
safety and economic benefits, so that there is a high demand on the automatic fault …
Process monitoring for material extrusion additive manufacturing: a state-of-the-art review
A Oleff, B Küster, M Stonis, L Overmeyer - Progress in Additive …, 2021 - Springer
Qualitative uncertainties are a key challenge for the further industrialization of additive
manufacturing. To solve this challenge, methods for measuring the process states and …
manufacturing. To solve this challenge, methods for measuring the process states and …
Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine
Fault diagnosis is the key procedure to ensure the stability and reliability of mechanical
equipment operation. Recent works show that deep learning-based methods outperform …
equipment operation. Recent works show that deep learning-based methods outperform …
Process monitoring, diagnosis and control of additive manufacturing
Additive manufacturing (AM) can build up complex parts in a layer-by-layer manner, which is
a kind of novel and flexible production technology. The special manufacturing capability of …
a kind of novel and flexible production technology. The special manufacturing capability of …
Dual-attention generative adversarial networks for fault diagnosis under the class-imbalanced conditions
Deep learning has been widely applied to intelligent fault diagnosis with balanced training
set. However, certain available fault data are extremely limited, resulting in an imbalanced …
set. However, certain available fault data are extremely limited, resulting in an imbalanced …
Self-adaptation graph attention network via meta-learning for machinery fault diagnosis with few labeled data
Effective application of fault diagnosis models requires that new fault types can be
recognized rapidly after they occur few times, even only one time. To this end, a self …
recognized rapidly after they occur few times, even only one time. To this end, a self …