Wedagate: Wireless Data Acquisition in Gas Turbine Engine Testing

Project: Research

Project Details


This collaborative project aims to develop capability required for deploying large-scale wireless sensor networks for data gathering during gas turbine engine testing. Currently, engine testing, requiring measurements of thousands of spatio-temporal parameter values, uses wired sensors connected via a cabling harness to remote condition-monitoring units. Such data acquisition requires many kilometers of wiring, involves long and expensive setup and instrumentation times and hinders efficient time-to-market. We aim to mitigate the challenges of deploying wireless sensors for data gathering in the harsh, dynamic and inaccessible environment of gas turbines involving high-speed rotations, rapid airflows, high temperatures and large amplitude vibrations.

Key findings

Wireless sensor networks (WSNs) represent a useful new technology for industrial data acquisition. However, the harsh environment of the gas turbine engine provides a number of challenges to deployment of wireless sensors. A physical layer channel model has been derived using a data-base derived from measurements made across the surface of a particular gas turbine engine. Scaling laws have been identified to allow the resulting models to be extrapolated to engines of other sizes. The model includes interference categories derived from aerospace standards in addition to propagation characteristics. The physical layer model lies at the heart of a WSN software simulator that has been developed to de-risk the selection/development/deployment of WSN technologies for wireless data acquisition in gas turbine engine testing. Based on the validated, realistic physical layer model, the simulator platform allows different communications protocols to be investigated and compared prior to commitment to hardware prototyping.
Effective start/end date1/11/0831/10/11


  • Technology Strategy Board TSB: £67,758.00


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.