- Level: Intermediate
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to Predictive Maintenance
Learning Outcome: Understand the basics of predictive maintenance.1.1 Overview of engineering maintenance processes.
1.2 Role of AI in maintenance prediction.
1.3 Types of data required for predictive systems.
Practical Component
Explore IoT sensor data from machines.
Week 2: Data Analysis for Maintenance Prediction
Learning Outcome: Process and analyze maintenance data.1.4 Basics of signal processing for AI.
1.5 Detecting patterns in machine performance.
1.6 Analyzing failures using historical data.
Practical Component
Use AI to find fault patterns in sample data.
Week 3: Machine Learning Models for Prediction
Learning Outcome: Build models to predict machine failures.1.7 Supervised learning models for prediction.
1.8 Feature selection for engineering data.
1.9 Deploying basic ML models.
Practical Component
Build and deploy a maintenance prediction model.
Week 4: Advanced AI Techniques
Learning Outcome: Use advanced AI methods for better predictions.1.10 Deep learning for predictive maintenance.
1.11 Time-series analysis for engineering data.
1.12 Real-time fault detection.
Practical Component
Build a time-series prediction model.
Week 5: Integration with IoT Systems
Learning Outcome: Combine AI with IoT for real-time solutions.1.13 Basics of IoT in predictive systems.
1.14 Real-time data streaming and processing.
1.15 Edge AI for on-site predictions.
Practical Component
Create a real-time maintenance solution.
Week 6: Final Project
Learning Outcome: Build and demonstrate a predictive maintenance system.1.16 Define a use case (e.g., machine wear detection).
1.17 Build and test the system.
1.18 Present the solution and its impact.
Practical Component
Demonstrate your AI-powered predictive system.