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Jinchen Ji

University of Technology Sydney · AU
Area of research
Computer Networks and Communications · Civil and Structural Engineering
Research interest
Research focused on Spall and Bearing (navigation), with related work in Discriminative model, Generalizability theory, Convolutional neural network. Notable publications include 'Digital twin-driven intelligent assessment of gear surface degradation', 'A review of vibration-based gear wear monitoring and prediction techniques', and 'Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics'.
h-index
citations
1,618
works
9
NIH funding
primary concept
email

Recent publications

State of Health Estimation for Second-Life Lithium-Ion Batteries in Energy Storage System With Partial Charging-Discharging Workloads
IEEE Transactions on Industrial Electronics 2024cited by 30position: middledoi
Sliding dynamics of a Filippov ecological system with nonlinear threshold control and pest resistance
Communications in Nonlinear Science and Numerical Simulation 2024cited by 11position: lastdoi
Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics
Mechanical Systems and Signal Processing 2023cited by 294position: middledoi
Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit
Reliability Engineering & System Safety 2023cited by 144position: middledoi
A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems
Mechanical Systems and Signal Processing 2023cited by 53position: middledoi
IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions
Reliability Engineering & System Safety 2023cited by 52position: middledoi
A Novel Weak Feature Extraction Method for Rotating Machinery: Link Dispersion Entropy
IEEE Transactions on Instrumentation and Measurement 2023cited by 33position: middledoi
Digital twin-driven intelligent assessment of gear surface degradation
Mechanical Systems and Signal Processing 2022cited by 513position: middledoi
A review of vibration-based gear wear monitoring and prediction techniques
Mechanical Systems and Signal Processing 2022cited by 488position: middledoi

Grants

No grants ingested yet.

Frequent collaborators

Ke Feng · National University of Singapore7 papers (2022–2023)Qing Ni · University of Electronic Science and Technology of China6 papers (2022–2023)Michael Beer · University of Liverpool3 papers (2022–2023)Yongchao Zhang · Zhejiang Sci-Tech University2 papers (2022–2023)Yadong Xu · Hong Kong Polytechnic University2 papers (2023–2023)Sheng Li · Xi'an Jiaotong University1 papers (2023–2023)Yulin Wang · Jinan University1 papers (2023–2023)Zheng Liu · Johns Hopkins University1 papers (2022–2022)Shun Wang · Northwestern Polytechnical University1 papers (2023–2023)Beibei Sun · East China University of Science and Technology1 papers (2023–2023)Yongbo Li · Northwestern Polytechnical University1 papers (2023–2023)Yiyue Jiang · University of Illinois Urbana-Champaign1 papers (2024–2024)Li Ding · Northwestern Polytechnical University1 papers (2023–2023)Yuqi Ke · Sun Yat-sen University1 papers (2024–2024)Yuxun Zhu · Jiangsu University1 papers (2024–2024)Weiwen Peng · Sun Yat-sen University1 papers (2024–2024)Zhengdi Zhang · Jiangsu University1 papers (2024–2024)Asoke K. Nandi · University of London1 papers (2023–2023)Benjamin Halkon · University of Technology Sydney1 papers (2023–2023)Hongtian Chen · Ministry of Education of the People's Republic of China1 papers (2023–2023)