C
Chao Liu
Researcher at Tsinghua University
Publications - 91
Citations - 2578
Chao Liu is an academic researcher from Tsinghua University. The author has contributed to research in topics: Rotor (electric) & Vibration. The author has an hindex of 19, co-authored 75 publications receiving 1517 citations. Previous affiliations of Chao Liu include Iowa State University.
Papers
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Fault diagnosis of wind turbine based on Long Short-term memory networks
TL;DR: Experimental results on two wind turbine datasets show that the proposed fault diagnosis framework is able to do fault classification effectively from raw time-series signals collected by single or multiple sensors and outperforms state-of-art approaches.
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Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application.
TL;DR: Wang et al. as mentioned in this paper proposed a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, by extending the marginal distribution adaptation to joint distribution adaptation (JDA).
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A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
TL;DR: A novel deep adversarial convolutional neural network (DACNN) is proposed, which contributes to making the feature representation robust, boosting the generalization ability of the trained model as well as avoiding overfitting with a small size of labeled samples.
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An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
TL;DR: This work presents a novel diagnosis framework that combines the spatiotemporal pattern network (STPN) approach with convolutional neural networks (CNN) to build a hybrid ST-CNN scheme, and it is verified that the spatial features can elevate the diagnosis accuracy.
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Short-term prediction of wind power using EMD and chaotic theory
TL;DR: In this paper, a hybrid prediction model with empirical mode decomposition (EMD), chaotic theory, and grey theory is constructed for short-term prediction of wind farm power, which has good prediction accuracy.