Qun He
Yanshan University
29 Papers
41 Citations
Qun He is an academic researcher from Yanshan University. The author has contributed to research in topics: Computer science & SCADA. The author has an hindex of 5, co-authored 13 publications.
Chat about Author
Papers
A Spatio-Temporal Multiscale Neural Network Approach for Wind Turbine Fault Diagnosis With Imbalanced SCADA Data
TL;DR: The proposed STMNN model can provide an end-to-end fault diagnosis solution with imbalanced SCADA data, and is evaluated through experiments on an SCADA dataset from a real wind farm, which has proved the effectiveness of the model in practical applications.
105
Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data
TL;DR: The proposed STFNN model provides an end-to-end fault diagnosis way, which can directly learn spatio-temporal dependency from the raw SCADA data and give the fault diagnosis result.
90
Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals
TL;DR: Wang et al. as discussed by the authors proposed a new multiview enhanced fault diagnosis framework to learn the correlated and complementary features across current and vibration signals, which are regarded as two different but related views.
52
DeepFedWT: A federated deep learning framework for fault detection of wind turbines
TL;DR: Wang et al. as mentioned in this paper designed a multi-scale residual attention network (MSRAN) model to extract informative features from raw multivariate sensor data, which first integrates a multiscale residual learning block to extract spatial features among different sensor variables at multiple scales and adopts a feature attention block to highlight important features highly associated with faults.
35
Dual residual attention network for remaining useful life prediction of bearings
TL;DR: Wang et al. as discussed by the authors proposed a dual residual attention network (DRAN) to extract degradation-sensitive features from complex vibration signals, and then adaptively identified important features contributing to bearing RUL prediction via a residual attention mechanism.
32