4 papers
HyBattNet: Hybrid Framework for Predicting the Remaining Useful Life of Lithium-Ion Batteries
Khoa Tran, Tri Le, Bao Huynh +4
Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric appli…
SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
Khoa Tran, Hung-Cuong Trinh, Vy-Rin Nguyen +2
Accurate battery modeling is essential for reliable state estimation in modern applications, such as predicting the remaining discharge time and remaining discharge energy in batte…
HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction
Khoa Tran, Bao Huynh, Tri Le +4
Accurate prediction of the Remaining Useful Life (RUL) in Lithium ion battery (LIB) health management systems is essential for ensuring operational reliability and safety. However,…
A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function
Khoa Tran, Lam Pham, Vy-Rin Nguyen +1
Motor bearing fault detection (MBFD) is critical for maintaining the reliability and operational efficiency of industrial machinery. Early detection of bearing faults can prevent s…