Agentic AI for Industrial Digital Twins: Predictive Maintenance, Smart Sensors & Autonomous Operations
Smarter Industry Through Connected Intelligence, Predictive Insight, and Autonomous Action.
About This Course
This workshop explores the integration of Agentic AI, Industrial Digital Twins, smart sensors, predictive analytics, and autonomous decision-making for intelligent industrial operations.
Participants will learn how real-time sensor data can be connected with digital twin models to monitor equipment performance, detect anomalies, predict failures, and support predictive maintenance and process optimization.
The workshop also introduces AI agents that can analyze industrial data, recommend maintenance actions, and automate selected operational decisions. It is ideal for participants interested in Industry 4.0, Industrial AI, IoT, smart manufacturing, automation, and intelligent asset management.
Aim
The aim of this workshop is to provide participants with practical knowledge of designing and implementing AI-powered industrial digital twin systems that combine smart sensing, machine learning, predictive maintenance, and agentic AI to enable intelligent monitoring, forecasting, optimization, and autonomous industrial decision-making.
Workshop Objectives
- 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.
Workshop Structure
🗓️ Day 1 — Industrial Digital Twins, Smart Sensors & IIoT
- Fundamentals of industrial digital twins and Industry 4.0
- Digital twin architecture and physical–virtual synchronization
- Smart sensors for temperature, vibration, pressure and equipment health
- Industrial IoT data acquisition and real-time monitoring
- Industrial connectivity using MQTT and OPC UA
- Sensor-data cleaning, preprocessing and feature extraction
- Equipment-health indicators and condition monitoring
- Digital twin visualization and asset-performance tracking
- Applications in manufacturing, energy, automotive and process industries
🛠️ Hands-On — Sensor-Driven Industrial Digital Twin
Task: Build a sensor-driven digital twin using the AI4I dataset to monitor operating conditions, calculate equipment-health indicators and visualize machine-failure risk.
Tools: Google Colab, Python, Pandas, NumPy, Scikit-learn, Matplotlib, Plotly
🗓️ Day 2 — Predictive Maintenance, Fault Diagnosis & Remaining Useful Life
- Preventive, condition-based and predictive maintenance strategies
- Machine-learning models for equipment-failure prediction
- Anomaly detection for abnormal machine behaviour
- Fault classification and early-warning indicators
- Handling imbalanced industrial failure data
- Equipment-degradation modelling
- Remaining Useful Life estimation
- Failure probability and maintenance-window prediction
- Explainable AI using SHAP and feature importance
- Translating AI predictions into maintenance insights
🛠️ Hands-On — Equipment Failure & RUL Prediction
Task: Analyze the NASA C-MAPSS dataset to model equipment degradation, predict failure risk, estimate Remaining Useful Life and explain maintenance predictions using SHAP.
Tools: Google Colab, Python, Pandas, Scikit-learn, XGBoost, SHAP, Matplotlib
🗓️ Day 3 — Agentic AI & Autonomous Industrial Decisions
- Fundamentals of agentic AI for industrial systems
- Perception, memory, reasoning, planning, action and feedback
- Connecting AI agents with digital twins and sensor data
- Autonomous fault-response and maintenance recommendations
- Maintenance scheduling and work-order prioritization
- Digital twin–based what-if simulations
- Run-to-failure versus planned-maintenance analysis
- Multi-agent monitoring, diagnostic and planning workflows
- Human-in-the-loop approval and safety guardrails
- Traceability, auditability and responsible industrial AI
- Industry 5.0 and human–AI collaboration
🛠️ Hands-On — Agentic AI Maintenance Decision Workflow
Task: Build an agentic workflow that analyzes equipment condition, identifies potential faults, compares maintenance scenarios and generates an actionable recommendation with human approval.
Tools: Google Colab, Python, Scikit-learn, SHAP, LangChain/LangGraph Concepts, LLM-Based Workflows
Who Should Enrol?
- 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
Important Dates
Registration Ends
September 18, 2026
IST 4.30 pm IST
Workshop Dates
September 18, 2026 – September 20, 2026
IST 5 :30 PM IST
Workshop Outcomes
- 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
Fee Structure
Student
₹2499 | $80
Ph.D. Scholar / Researcher
₹3499 | $90
Academician / Faculty
₹4499 | $105
Industry Professional
₹6499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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