AI for Predictive Maintenance of Energy Assets
A two-day program on AI-driven predictive maintenance for energy sector assets — from rotating equipment to transformers and switchgear.
Course Overview
Energy assets — from rotating equipment to transformers — represent significant capital investments where predictive maintenance can deliver major savings. This program focuses on AI-driven predictive maintenance specifically for energy sector assets, covering sensor data processing, failure prediction, and health monitoring for critical equipment types.
Target Audience
Maintenance engineers, reliability engineers, asset managers in the energy sector
Prerequisites
EN-101 or equivalent. Energy operations or maintenance background.
Learning Objectives
- Apply predictive maintenance to energy assets
- Process and analyze energy system sensor data
- Build failure prediction models for rotating equipment
- Monitor transformer and switchgear health
- Integrate predictions with asset management
Key Modules
- Predictive Maintenance for Energy Assets
- Sensor Data from Energy Systems
- Vibration and Thermal Analysis with ML
- Failure Prediction for Rotating Equipment
- Transformer and Switchgear Health Monitoring
- Integration with Asset Management Systems
- Case Studies in Energy Asset Maintenance
Learning Outcomes
- A predictive maintenance framework for energy assets
- Practical skills in energy sensor data analysis
- Understanding of failure prediction for key equipment
- A roadmap for asset health monitoring deployment
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
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