Industrial Digital Twins : AI, IoT, Predictive Maintenance & Autonomous Smart Manufacturing

To equip participants with the practical knowledge and computational skills required to design AI-powered industrial digital twins for machine-health monitoring, predictive maintenance, what-if simulation and intelligent manufacturing decision support.

The workshop is designed to help participants:

• Understand Digital Twin 2.0 architecture and industrial applications
• Simulate Industrial IoT data and machine-operating conditions
• Develop machine-health indicators from multi-sensor data
• Build AI models for anomaly detection and failure prediction
• Apply explainable AI to identify factors responsible for equipment failure
• Analyze streaming data and changing machine conditions
• Perform digital-twin-based what-if simulations
• Design risk-based maintenance alerts and automated decision rules
• Understand safe, human-in-the-loop autonomous manufacturing systems

📅 Day 1: Industrial Digital Twins & IoT Integration

• Focus: Understanding Industrial Digital Twin architectures, IoT connectivity, real-time data synchronization, and industrial asset modeling.
• Understanding digital models, digital shadows, and digital twins in industrial applications.
• Exploring asset-level, process-level, and factory-level digital twin architectures.
• Introduction to Digital Twin 2.0 architecture, lifecycle management, and intelligent industrial systems.
• Industrial IoT sensors, machine data acquisition, and operational data streams.
• Understanding MQTT, OPC UA, edge computing, and cloud connectivity for industrial systems.
• Real-time data synchronization, machine-health indicators, and equipment operating states.
• Data quality, interoperability, cybersecurity, and challenges in industrial digital twin deployment.

🛠️ Hands-on: Build an IoT-Enabled Digital Twin

• Explore AI4I industrial sensor data for machine-health analysis.
• Simulate machine operations and industrial processes using SimPy.
• Visualize machine-health states and operational behavior using interactive dashboards.

🧰 Tools Covered: Python, AI4I Dataset, SimPy, Plotly, MQTT, OPC UA

📌 Output: Interactive machine-health digital twin.

📅 Day 2: AI-Based Predictive Maintenance & Fault Diagnosis

• Focus: Applying machine learning for predictive maintenance, fault detection, failure prediction, and explainable industrial decision-making.
• Understanding reactive, preventive, and predictive maintenance strategies.
• Industrial sensor-data preprocessing and feature engineering for machine-health analysis.
• Anomaly detection, fault classification, and equipment-failure prediction approaches.
• Remaining Useful Life (RUL) estimation for industrial assets.
• Handling imbalanced failure datasets and improving model reliability.
• Model validation using performance metrics for predictive-maintenance systems.
• Explainable AI approaches for understanding fault predictions.
• Integration of AI predictions with digital-twin states for intelligent maintenance workflows.

🛠️ Hands-on: Develop an Explainable Failure-Prediction Model

• Train a predictive-maintenance model using Scikit-learn.
• Evaluate equipment-failure probability using machine-learning metrics.
• Interpret model predictions using SHAP explainability techniques.

🧰 Tools Covered: Python, Scikit-learn, Pandas, NumPy, SHAP, Matplotlib

📌 Output: Explainable AI model for equipment-failure prediction.

📅 Day 3: Autonomous Smart Manufacturing & Prescriptive Maintenance

• Focus: Exploring autonomous industrial systems, prescriptive maintenance, streaming analytics, and AI-driven manufacturing optimization.
• Understanding predictive versus prescriptive maintenance approaches.
• Streaming analytics and online machine learning for real-time industrial monitoring.
• Managing concept drift in changing machine conditions and dynamic production environments.
• Digital-twin-based what-if simulation and scenario analysis.
• Risk-based maintenance prioritization and automated decision rules.
• Human-in-the-loop autonomous systems and intelligent manufacturing workflows.
• Production optimization, energy efficiency, and smart manufacturing strategies.
• Introduction to Agentic AI and reinforcement-learning concepts for industrial automation.
• Safety, explainability, and Industry 5.0 governance considerations.

🛠️ Hands-on: Build an Autonomous Maintenance Decision System

• Analyze streaming sensor data using River for online machine learning.
• Perform what-if risk simulations using digital twin scenarios.
• Generate intelligent maintenance recommendations through an interactive Gradio interface.

🧰 Tools Covered: Python, River, Gradio, Digital Twin Simulation, Reinforcement Learning Concepts

📌 Output: Interactive maintenance decision-support prototype.

This workshop is suitable for:

• Researchers and research scientists
• Faculty members and academicians
• PhD scholars and postgraduate students
• Mechanical, electrical, manufacturing and industrial engineers
• Maintenance and reliability professionals
• Automation and control-system engineers
• Industrial IoT developers
• AI, machine-learning and data-science professionals
• Production and operations professionals
• Industry 4.0 and smart-manufacturing practitioners
• Professionals working in automotive, aerospace, energy, manufacturing and process industries
• Anyone interested in digital twins, predictive maintenance or autonomous manufacturing

After completing the workshop, participants will be able to:

• Differentiate digital models, digital shadows and connected digital twins
• Design a data-driven industrial digital-twin workflow
• Process temperature, torque, speed and tool-wear data
• Simulate machine operations and sensor events using SimPy
• Create interactive machine-health visualizations using Plotly
• Train and evaluate equipment-failure prediction models
• Explain AI predictions using SHAP
• Analyze streaming sensor data using River
• Conduct what-if simulations for operational-risk assessment
• Generate prioritized maintenance recommendations
• Build an interactive decision-support interface using Gradio
• Apply the workflow to academic research and industrial use cases