Researchers Deploy Predictive Maintenance Model For Infrastructure
Edward Khomotso Nkadimeng and teams at Stellenbosch University and NRF-iThemba LABS describe a predictive maintenance model built to monitor research equipment and industrial assets using low-cost IoT sensors and cloud AI. The system analyses vibration, temperature, pressure and voltage to predict failures, and similar approaches have cut unplanned downtime by 20%-40%. The model aims to improve resilience in African labs, utilities and industry.
Key Points
- 1Demonstrates a low-cost IoT-based predictive maintenance model for pumps, turbines and lab instruments
- 2Shows failures follow recognisable vibration, temperature, pressure and voltage signatures enabling early detection
- 3Enables targeted maintenance scheduling, reducing downtime, saving parts and improving infrastructure resilience
Scoring Rationale
Practical implemented system with measurable downtime benefits; limited novelty compared with existing industrial predictive-maintenance approaches.
Sources
Public references used for this report.
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