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AI · Moderate · Video-based learning

Agentic AI for Industrial Digital Twins: Predictive Maintenance, Smart Sensors & Autonomous Operations

Smarter Industry Through Connected Intelligence, Predictive Insight, and Autonomous Action.

★ 4.2 (26 ratings)Access 24/7 · 365 daysVideo lectures of 3+ hrsCertificate
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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.

Course 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.
Next: Syllabus →

Syllabus

🗓️ 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

Course 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
Next: Fees →

Course fee

Learners in India

₹2,499

Learners outside India

$50

  • Video lectures
  • Study material
  • Certificate after completion
  • 365-day access

Enrol in the course

The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

How is this course taught?

Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.

How long do I have access?

365 days from your registration, on your NanoSchool dashboard.

Do I get a certificate?

Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
 WhatsApp +91 99581 61117

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