Predictive AI for Offshore Wind & Marine Renewable Energy Asset Health Modeling
Combining oceanographic data and mechanical failure logs to forecast maintenance for open-ocean renewable energy assets.
Skills you will gain:
About Program:
This program focuses on using Predictive AI, data analytics, and condition monitoring techniques to assess, predict, and improve the health and reliability of offshore wind and marine renewable energy assets.
Aim: The aim of this program is to equip learners with practical knowledge of how Artificial Intelligence can be applied to monitor offshore wind turbines, tidal systems, wave energy devices, and marine renewable infrastructure for early fault detection, predictive maintenance, performance optimization, and asset life extension.
Program Objectives:
- Understand offshore wind and marine renewable energy asset challenges.
- Learn how sensor, SCADA, weather, and operational data are used for health modeling.
- Explore AI methods for fault detection, anomaly detection, and failure prediction.
- Understand predictive maintenance and remaining useful life estimation.
- Learn how AI can reduce downtime, improve safety, and extend asset lifespan.
What you will learn?
📅 Day 1: Foundations of Offshore Renewable Asset Health Monitoring
Topics Covered:
- Introduction to offshore wind and marine renewable energy assets
- Key asset health challenges: corrosion, fatigue, vibration, and blade damage
- Role of Predictive AI in asset monitoring and maintenance planning
- Understanding SCADA, sensor, weather, vibration, and operational data
- Basics of condition monitoring and early fault detection
🛠️ Hands-on Activity:
SCADA-Based Anomaly Detection in Offshore Wind Turbine Data
Participants will use Python in Google Colab to clean sample turbine sensor data and detect abnormal operating patterns using basic machine learning methods.
📅 Day 2: Predictive AI Models for Fault Detection and Asset Degradation
Topics Covered:
- Machine learning for fault classification and failure prediction
- Time-series analysis for turbine and marine asset health data
- Remaining Useful Life estimation for offshore components
- AI models for vibration, temperature, power output, and fatigue trend analysis
- Explainable AI for maintenance decision-making
🛠️ Hands-on Activity:
Failure Prediction and Remaining Useful Life Modeling
Participants will build a simple predictive model in Google Colab to estimate asset degradation risk and remaining useful life using simulated sensor and operational data.
📅 Day 3: Digital Twins, Risk Scoring, and Intelligent Maintenance Planning
Topics Covered:
- Digital twin concepts for offshore wind and marine renewable assets
- Integrating AI, IoT, and real-time monitoring for asset health modeling
- Risk-based inspection and predictive maintenance planning
- AI for reducing downtime, improving safety, and extending asset lifespan
- Future trends: physics-informed AI, edge monitoring, autonomous inspection, and marine energy analytics
🛠️ Hands-on Activity:
Asset Health Risk Dashboard in Google Colab
Participants will create a notebook-based asset health dashboard showing fault risk score, maintenance priority, and visual trends for offshore renewable energy components.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Researchers in offshore wind, marine energy, AI, and predictive maintenance.
- Academicians and faculty working in renewable energy and reliability engineering.
- Ph.D. scholars and postgraduate students in energy, AI, ML, or sustainability.
- Industry professionals from offshore wind, marine energy, and power sectors.
- Engineers working with SCADA, sensor data, fault detection, and maintenance planning.
- Data scientists interested in renewable energy asset health modeling.
- Professionals interested in digital twins, risk scoring, and failure prediction.
Career Supporting Skills
Program Outcomes