Liuyang Song - Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-end Mechanical Equipment, Beijing University of Chemical Technology

基本信息

Career Trajectory

Bio

Liuyang Song (Member, IEEE) received the Ph.D. degree from Mie University, Tsu, Japan, in 2017. She is currently an Associate Professor with the College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China. Her current research interests include signal processing, equipment fault diagnosis, and performance degradation assessment technology.

Research Interests

Papers共 93 篇Author StatisticsCo-AuthorSimilar Experts

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A Transferability-Enhanced and Structural Information Mining Network for Cross-Domain Fault Diagnosis

Huaqing Wang, Shikuan Zhang, Pengyuan Hao, Na Wu,Changkun Han,Liuyang Song

IEEE Sensors Journalno. 99 (2024): 1-1

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A Convolutional Neural Network with Hybrid Loss Function for Bearing Fault Diagnosis

Dongrui Lv, Zhenbao Fu, Zhengchang Su, Haitao Ni,Huaqing Wang,Liuyang Song

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) Mechanisms and Machine Sciencepp.665-675, (2024)

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Advancing RUL prediction in mechanical systems: A hybrid deep learning approach utilizing non-full lifecycle data

Tianjiao Lin,Liuyang Song,Lingli Cui,Huaqing Wang

Advanced Engineering Informatics（2024）

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Fault Diagnosis Algorithm Based on Lightweight Convolutional Neural Network

Jiachen Liu, Jiaxun Du, Pengyuan Hao,Huaqing Wang, Dingjie Kong,Liuyang Song

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) Mechanisms and Machine Sciencepp.843-852, (2024)

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A Semi-Supervised GCN Framework for Transfer Diagnosis Crossing Different Machines

Liuyang Song, Pengyuan Hao, Shikuan Zhang,Changkun Han,Huaqing Wang

IEEE Sensors Journalno. 99 (2024): 1-1

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Advancements in bearing remaining useful life prediction methods: a comprehensive review

Liuyang Song,Tianjiao Lin, Ye Jin, Shengkai Zhao, Ye Li,Huaqing Wang

MEASUREMENT SCIENCE AND TECHNOLOGYno. 9 (2024)

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Remaining Useful Life Prediction Method Enhanced by Data Augmentation and Similarity Fusion

Huaqing Wang, Ye Li, Ye Jin, Shengkai Zhao,Changkun Han,Liuyang Song

Vibrationno. 2 (2024): 560-581

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Improved Shift-Invariant Sparse Parsing of Mechanical Fault Based on Feature Atom

Changkun Han, Wei Lu,Lingli Cui,Liuyang Song,Huaqing Wang

IEEE Transactions on Instrumentation and Measurementno. 99 (2024): 1-1

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A lightweight improved residual neural network for bearing fault diagnosis

Huaqing Wang, Zhenbao Fu,Tianjiao Lin,Changkun Han,Wenbo Zhang,Liuyang Song

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE (2024)

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A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

Huaqing Wang,Wenbo Zhang,Changkun Han, Zhenbao Fu,Liuyang Song

MEASUREMENT SCIENCE AND TECHNOLOGYno. 4 (2024)

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