01/09/2026
Machine fault data is often unavailable until a machine has been operating in the field. Without representative fault data, developing and testing fault-classification algorithms can be challenging.
The session shows how multi-domain digital twins help simulate machine faults, generate synthetic fault data, and develop fault-classification algorithms for condition monitoring.
- Model mechanical and electrical fault scenarios using a flow pack machine example
- Generate representative datasets and identify diagnostic features for fault classification
- Develop supervised models to classify operating and fault conditions
- Connect physical modelling with algorithm development
📅 7 October 2026 | 10:00 CEST
Using Multi-Domain Digital Twins to Develop Fault Classification Algorithms
👉 Register for the session: https://spr.ly/6184B1AbBm
This webinar demonstrates how a multi-domain digital twin can be used to simulate machine faults, generate synthetic fault data, extract diagnostic features, and develop fault detection algorithms. Using a flow pack machine example, attendees will see how mechanical and electrical fault scenarios ca...
