Agentic AI–Powered Industrial Digital Twins: Smart Sensors, Predictive Maintenance & Autonomous Decision Systems

• Understand the fundamentals and architecture of Industrial Digital Twins in Industry 4.0.
• Explore the integration of smart sensors, IIoT, and real-time data with digital twin systems.
• Learn industrial data preprocessing, feature extraction, and condition monitoring.
• Apply machine learning and predictive analytics for anomaly detection, fault diagnosis, and failure prediction.
• Develop predictive maintenance models using RUL, health indicators, and failure probability.
• Explore Agentic AI for industrial monitoring, reasoning, and action planning.
• Design autonomous workflows for maintenance, fault response, and process optimization.
• Evaluate digital twin models using relevant performance and reliability metrics.

📅 Day 1 – Industrial Digital Twins, Smart Sensors & IIoT

• Industrial Digital Twin Fundamentals
• Architecture, physical–virtual synchronization, and Industry 4.0 applications
• Smart Sensors & IIoT Integration
• Real-time sensor data, MQTT, OPC UA, and industrial connectivity
• Industrial Data Processing
• Sensor preprocessing, feature extraction, health indicators, and condition monitoring
• 🧪 Hands-on: Build a Sensor-Driven Industrial Digital Twin using AI4I Dataset in Google Colab

📅 Day 2 – Predictive Maintenance, Fault Detection & RUL

• AI for Predictive Maintenance
• Machine learning for equipment failure and maintenance prediction
• Anomaly Detection & Fault Diagnosis
• Detect abnormal behaviour and classify equipment faults
• Remaining Useful Life (RUL)
• Asset-health prediction, degradation modelling, and failure probability
• Explainable AI
• Interpret model predictions using SHAP and feature importance
• 🧪 Hands-on: Build an Equipment Failure & RUL Prediction Model using NASA C-MAPSS in Google Colab

📅 Day 3 – Agentic AI & Autonomous Industrial Decisions

• Agentic AI for Industrial Systems
• Perception, reasoning, planning, action, and feedback
• Autonomous Maintenance Decisions
• Fault response, maintenance scheduling, and operational optimization
• Digital Twin What-If Simulation
• Simulate maintenance and operating scenarios before action
• Multi-Agent & Human-in-the-Loop Systems
• Coordinated AI agents, safety controls, and Industry 5.0 applications
• 🧪 Hands-on: Build an Agentic AI Maintenance Decision Workflow in Google Colab

• Explain the architecture and workflow of an AI-powered Industrial Digital Twin.
• Integrate industrial sensor data with virtual models of machines and processes.
• Analyze sensor and time-series data for condition monitoring and anomaly detection.
• Build predictive models for failure prediction and predictive maintenance.
• Assess asset health using Remaining Useful Life (RUL) and health indicators.
• Develop AI-assisted workflows for fault identification and corrective action recommendations.
• Understand how Agentic AI can reason over industrial data and digital twin outputs.
• Design decision pipelines for maintenance prioritization, process optimization, and automated response.
• Interpret model predictions for practical industrial decision-making.
• Apply these concepts to smart factories, industrial equipment, energy assets, and cyber-physical systems

• Researchers & PhD Scholars
• Academicians & Faculty Members
• Industry Professionals
• Mechanical, Electrical & Mechatronics Engineers
• AI/ML & Data Science Professionals
• IoT & Automation Engineers
• Maintenance & Reliability Engineers
• Industry 4.0 / Digital Transformation Professionals