Rotating machinery fault diagnosis under time-varying speeds: A review
D Liu, L Cui, H Wang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …
and harsh environments make its key parts, such as rolling bearings and gears, prone to …
Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review
Y Sun, W Wang - Engineering Failure Analysis, 2023 - Elsevier
In the modern manufacturing industry, mechanical equipment plays a crucial role.
Equipment working in harsh environments for a long time is more likely to break down …
Equipment working in harsh environments for a long time is more likely to break down …
Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning
Offshore wind turbines play a vital role in transferring wind energy to electricity, which could
help relieve the energy crisis and improve the global climate. In general, offshore wind …
help relieve the energy crisis and improve the global climate. In general, offshore wind …
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 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) …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Feature extraction using parameterized multisynchrosqueezing transform
X Li, H Zhao, L Yu, H Chen, W Deng… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Parametrized time-frequency analysis (PTFA) can effectively improve time-frequency energy
aggregation of non-stationary signal and immunity of cross term interference, but it exists the …
aggregation of non-stationary signal and immunity of cross term interference, but it exists the …
Diagnosing rotating machines with weakly supervised data using deep transfer learning
Rotating machinery fault diagnosis problems have been well-addressed when sufficient
supervised data of the tested machine are available using the latest data-driven methods …
supervised data of the tested machine are available using the latest data-driven methods …
Semisupervised graph convolution deep belief network for fault diagnosis of electormechanical system with limited labeled data
The labeled monitoring data collected from the electromechanical system is limited in the
real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory …
real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory …
Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places
In the recent years, data-driven machinery fault diagnostic methods have been successfully
developed, and the tasks where the training and testing data are from the same distribution …
developed, and the tasks where the training and testing data are from the same distribution …