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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 …
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 …
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 …
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) …
MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios
M Ye, X Yan, D Jiang, L **ang, N Chen - Knowledge-Based Systems, 2024 - Elsevier
Owing to the harsh operating environment of rolling bearings, acquired vibration signals
contain strong noise interference, which makes it challenging for conventional methods to …
contain strong noise interference, which makes it challenging for conventional methods to …
An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples
Previous deep learning-based fault diagnosis methods for planetary gearbox require
numerous training samples and lack the necessary interpretability. Aiming at the problems of …
numerous training samples and lack the necessary interpretability. Aiming at the problems of …
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 …
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 …
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) …