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Bi-Liang Lu
Researcher at Hunan University of Science and Technology
Publications - 10
Citations - 211
Bi-Liang Lu is an academic researcher from Hunan University of Science and Technology. The author has contributed to research in topics: Deep learning & Fault (power engineering). The author has an hindex of 4, co-authored 10 publications receiving 57 citations.
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Journal ArticleDOI
Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis
TL;DR: A deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
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A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines
TL;DR: Detailed comparisons and extensive experimental results show that the diagnosis performance of SPADA outperforms the existing deep learning and domain adaptation methods in dealing with the PDA problem.
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Fault Diagnosis for Electromechanical Drivetrains Using a Joint Distribution Optimal Deep Domain Adaptation Approach
TL;DR: Experimental results show that the JDDA can achieve better performance compared with the reference machine learning, deep learning and domain adaptation approaches.
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A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings
TL;DR: Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing and indicate that E- LSTM can achieve better performance.
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A Deep Adversarial Learning Prognostics Model for Remaining Useful Life Prediction of Rolling Bearing
TL;DR: A deep adversarial long short-term memory (LSTM) prognostic framework is proposed to overcome the major issue related to prediction error superposition, and a generative adversarial network (GAN) architecture combining the LSTM network and autoencoder (AE) is investigated for bearing RUL monitoring.