Project Details
Description
Funded under FP7-TRANSPORT.
Reducing the amount of instrument cabling on turbine engines is key to more efficient testing, and will enable reduced wiring weight and complexity on production engines in the future.
It is anticipated that wireless capability for Engine Health Monitoring (EHM) sensors could significantly reduce the cost and complexity of retrofitting improved EHM capabilities to existing engines in service, as well as providing capability for future product prognostic and diagnostic health management systems. Enablers for fully wireless, less-wired and mixed networked sensors will be developed.
Reducing the amount of instrument cabling on turbine engines is key to more efficient testing, and will enable reduced wiring weight and complexity on production engines in the future.
It is anticipated that wireless capability for Engine Health Monitoring (EHM) sensors could significantly reduce the cost and complexity of retrofitting improved EHM capabilities to existing engines in service, as well as providing capability for future product prognostic and diagnostic health management systems. Enablers for fully wireless, less-wired and mixed networked sensors will be developed.
| Short title | STARGATE |
|---|---|
| Status | Finished |
| Effective start/end date | 1/11/12 → 29/02/16 |
Collaborative partners
- Coventry University
- Meggitt (UK) Limited (lead)
- Rolls-Royce plc
- Safran Aircraft Engines
- GKN Aerospace Sweden AB
- Siemens
- The von Karman Institute for Fluid Dynamics
- Oxsensis Limited
- Office National d'Etudes et de Recherches Aerospatiales
- University of Cambridge
- Chalmers University of Technology
- Scitek Consultants Limited
- Bayerische Zentrum für Angewandte Energieforschung e.V.
- Loughborough University
Keywords
- Wireless sensing
- Electronics
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Research output
- 1 Paper
-
Optimising Low Power Dual Prediction Systems
Kemp, J., Gaura, E., Allen, M. P. & Brusey, J., 2015, p. 7-10.Research output: Contribution to conference › Paper › peer-review
Open Access