Abstract
Digital Twins are a valuable tool for managing the health of industrial components, thanks to their ability to detect and diagnose failures. This paper presents a novel Digital Model as the foundational step towards developing a Digital Twin. It enables the detection of broken tooth failure in wind turbine drive-trains by using wind data and vibration analysis. The model is projected into the future as a monitoring tool by allowing visual indicators of failure patterns to be identified and subsequently compared with real signals of this critical component.
| Original language | English |
|---|---|
| Pages (from-to) | 25-30 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 58 |
| Issue number | 8 |
| DOIs | |
| State | Published - 1 Jun 2024 |
| Event | 6th IFAC Workshop on Advanced Maintenance Engineering, Services and Technology, AMEST 2024 - Cagliari, Italy Duration: 12 Jun 2024 → 14 Jun 2024 |
Bibliographical note
Publisher Copyright:Copyright © 2024 The Authors.
Keywords
- broken tooth
- DM
- gearbox
- torsional vibration
- wind turbine
CACES Knowledge Areas
- 827A Industrial maintenance
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