Signal, Image and Video Processing, 2025
Selected research
Focused on battery health and machinery reliability.
01 / Machinery
Machinery reliability research
Software methods for fault detection and remaining useful life estimation in rotating equipment.
2025
2024
Mathematics, 2024
Estimate remaining useful life in changing industrial conditions.
Read machine research02 / Battery
Battery management research
Software techniques for RUL, SOH, SOC, and RDS estimation across lithium-ion systems.
2026
Preprint: arXiv, 2026
Estimate lithium-ion battery charge accurately throughout discharge.
Read C2L-Net
Preprint: SSRN, 2026
Track practical battery discharge progress with relative discharge stages.
Read RDS classification
Preprint: arXiv, 2026
Joint Remaining Useful Life Prediction and Capacity Estimation of Lithium-Ion Batteries Using Partial-Charging Data
Read partial-charging research2025
PLOS ONE, 2025
Maintain battery prediction accuracy when data conditions shift.
Read cross-domain research
Preprint: arXiv, 2025
Model battery discharge voltage with aging-aware discrete-state learning.
Read SeqBattNet
About AIWARE
From signals to operational confidence.
AIWARE began in 2022 with a clear purpose: apply advances in artificial intelligence to help industry identify equipment risks earlier.
We build predictive intelligence around the real operating signals of critical assets. Our work spans machinery health and lithium-ion battery systems, helping teams reduce avoidable downtime, protect equipment, and make safer, more efficient decisions.
Machinery intelligenceBattery intelligence
What guides us
See clearly
Make complex equipment behavior visible in the signals teams already collect.
Build rigorously
Develop models that remain useful amid noisy data and changing operating conditions.
Work openly
Partner closely, communicate plainly, and keep every decision tied to outcomes.
AIWARE Team
Core team
Applied research and engineering, turning complex signals into reliable insights.
Leadership
CEO
Khoa Tran
Khoa leads AIWARE's work in predictive intelligence for battery health and machinery reliability.
Leadership
CTO
Tri Le
Tri leads AIWARE's technology development for reliable, data-driven intelligence across industrial assets and battery systems.
Engineering
AI Engineer
Phone Wai Yan Moe
Phone applies advanced AI models to battery modeling, helping AIWARE turn complex battery data into reliable predictions.
Collaborators
Research collaborators
Specialist researchers who strengthen the rigor of our work.
Hung-Cuong Trinh
PhD in AI
Hung-Cuong applies AI to bioinformatics and computational biology.
25 publications196 citations
Lam Pham
PhD in AI
Lam applies AI to lung-sound and vibration-signal analysis.
86 publications1,602 citations
Quoc Bao Pham
PhD in AI
Quoc Bao applies AI to environmental research and water-resource management.
97 publications2,532 citations
Duong Tran Anh
PhD in AI
Duong applies AI to environmental, hydrology, and water-resource research.
133 publications4,455 citations
Ho-Si-Hung Nguyen
PhD in Automation
Ho-Si-Hung applies AI to electrical-signal analysis for machine maintenance.
43 publications180 citations
Hung Viet Pham
PhD in AI
Hung applies AI to industrial systems and engineering applications.
24 publications131 citations