Data preprocessing techniques in convolutional neural network based on fault diagnosis towards rotating machinery
S Tang, S Yuan, Y Zhu - IEEE Access, 2020 - ieeexplore.ieee.org
Rotating machinery plays a critical role in many significant fields. However, the
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …
Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …
Wavelet transform for rotary machine fault diagnosis: 10 years revisited
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
Limited condition monitoring data are recorded with label information in practice, which
make the fault identification task more challenging. A semi-supervised learning (SSL) …
make the fault identification task more challenging. A semi-supervised learning (SSL) …
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 …
Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction
As an important component of industrial equipment, once gears have failures, they may
cause serious catastrophes. Thus, the prediction of gear remaining life is of great …
cause serious catastrophes. Thus, the prediction of gear remaining life is of great …
Long-term gear life prediction based on ordered neurons LSTM neural networks
Gear failure may affect the operation of mechanical equipment, and even cause the
catastrophic break of machine and even casualties. Thus, the remaining useful life (RUL) …
catastrophic break of machine and even casualties. Thus, the remaining useful life (RUL) …
Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes
The domain adaptation (DA) model, aiming to solve the task of unlabeled or less-labeled
target domain fault classification through the training of labeled source domain fault data, is …
target domain fault classification through the training of labeled source domain fault data, is …
Small sample fault diagnosis method for wind turbine gearbox based on optimized generative adversarial networks
Y Su, L Meng, X Kong, T Xu, X Lan, Y Li - Engineering Failure Analysis, 2022 - Elsevier
Fault diagnosis of gearbox in engineering can effectively improve operational efficiency and
reduce maintenance costs. In this paper, a small sample diagnosis method based on …
reduce maintenance costs. In this paper, a small sample diagnosis method based on …
Gear fault diagnosis based on CS-improved variational mode decomposition and probabilistic neural network
Y Lin, M Xiao, H Liu, Z Li, S Zhou, X Xu, D Wang - Measurement, 2022 - Elsevier
Increasing the rate of gear fault diagnosis is crucial to research on gear fault diagnosis
methods. The existing signal processing methods have modal aliasing phenomena and …
methods. The existing signal processing methods have modal aliasing phenomena and …