PHM Theory and Methods
- • Condition-based and Predictive Maintenance
- • Explainable and Trustworthy Detection, Diagnostics & Prognostics Methods
- • Artificial Intelligence (including Machine Learning and Deep Learning)
- • Data-driven, Model-based, and Hybrid Methodologies for Modeling of Degradation
- • Physics of Failure
- • Fault-Adaptive Control
PHM Design and Integration
- • Digital Twin and Digital Thread
- • IoT for PHM Cost-Effectiveness and Return-on-Investment Analysis
- • Failure Mode, Effects & Critical Analysis
- • Smart Manufacturing (Planning, Automation, Device, Sensor)
- • Decision Support Systems
- • Software and Hardware for PHM
- • Standards for PHM
PHM, Foundation Models & Emerging AI
- • Large Language Models for PHM and Manufacturing
- • Foundation Models and Time-Series Foundation Models for PHM
- • Multi-modal AI and Vision-Language Models for PHM
- • Generative AI for PHM and Intelligent Maintenance
- • Agentic AI for PHM and Autonomous Decision-Making
- • Retrieval-Augmented Generation, Knowledge Graphs, and Knowledge-Enhanced AI
- • Self-Supervised, Continual-Learned, and Few-Shot Learning
- • Trustworthy, Safe, Robust, and Explainable AI for PHM
- • Edge AI and On-device Intelligence for Real-time PHM
- • Human-AI Collaboration and Human-in-the-loop PHM
- • Synthetic Data and Digital-Twin-based AI for PHM