From Data to Reliability: Predictive Monitoring of Asynchronous Motors
As industrial systems demand higher reliability and reduced downtime, predictive monitoring is becoming a key enabler in electric motor operation. This session presents a practical approach to monitoring asynchronous motors through the integration of electrical, thermal, and vibration data. It explores how real-time visualisation and control systems support early fault detection and improved operational performance, bridging the gap between research and industrial application.
This work is supported by the European Union – NextGenerationEU programme.
The presentation will also briefly highlight the Electrical Engineering Department’s collaboration with industry through Innovation, Research and Implementation (IRI) projects, demonstrating the practical application of research in real-world engineering contexts.
- Integration of multi-parameter data (current, voltage, power, temperature, vibration) for comprehensive motor condition monitoring
- Identification of early fault indicators through analysis of deviations under real operating conditions
- Role of real-time control and visualisation platforms (e.g. TwinCAT) in enabling predictive maintenance strategies
- Practical implications for improving reliability, reducing downtime, and supporting smarter motor operation
Wednesday 20 May 13:20 - 13:50 Electric Motor Forum Stage
Industrial AI and Digitalisation
Speakers
Head of the Electrical Engineering Department, Zagreb University of Applied Sciences
