AI for Predictive Maintenance
A two-day program on building predictive maintenance systems with AI — from data collection to model deployment and action.
Course Overview
Predictive maintenance is one of the highest-ROI applications of industrial AI. This program covers the full lifecycle of a predictive maintenance system — from sensor data collection and feature engineering to model development, deployment, and integration with maintenance workflows. Participants learn through real-world examples how AI is reducing downtime and maintenance costs.
Target Audience
Maintenance engineers, reliability engineers, operations engineers, data scientists
Prerequisites
IND-101 or equivalent. Understanding of industrial maintenance.
Learning Objectives
- Design a predictive maintenance system architecture
- Collect and engineer features from sensor data
- Build classification and regression models for failure prediction
- Deploy models for real-time inference
- Integrate predictions with maintenance workflows
Key Modules
- Predictive Maintenance Fundamentals
- Sensor Data Collection and Preprocessing
- Feature Engineering for Equipment Health
- Failure Prediction Models
- Remaining Useful Life Estimation
- Real-Time Inference and Alerting
- Integration with CMMS and Maintenance Workflows
- Case Studies in Predictive Maintenance
Learning Outcomes
- A predictive maintenance system architecture
- Practical skills in sensor data feature engineering
- Understanding of failure prediction and RUL models
- A framework for integrating AI with maintenance operations
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
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