Analysis
Predictive Maintenance for Smart Factory Conveyors
Overview
What this challenge is about.
Your task is to design a predictive maintenance system for AutoParts' conveyor belts. Use vibration and temperature sensors to monitor motor health. Data should be processed at the edge for real-time alerts and sent to the cloud for historical analysis. Success means predicting failures at least 48 hours in advance with 90% accuracy. Constraints: no changes to existing PLCs, use MQTT for data transmission, and keep sensor cost under €200 per conveyor. Deliverables include sensor placement plan, edge vs. cloud processing decision, ML model specification, and a cost-benefit analysis.
The Brief
What you'll do, and what you'll demonstrate.
Design a predictive maintenance system using IoT sensors to predict conveyor failures in a smart factory.
Earning criteria — what you'll demonstrate
- Design an IoT sensor network for condition monitoring in an industrial setting
- Evaluate edge vs. cloud computing trade-offs for real-time analytics
- Apply machine learning techniques (e.g., anomaly detection, regression) to predictive maintenance
- Calculate return on investment for IoT solutions
- Integrate IoT data with existing industrial control systems
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.