Rolls-Royce

Locating and characterising damage on fan blades of a gas turbine engine is critical in the aerospace industry to ensure safe operation. Two MEng students’ group projects investigate automated methods for locating and classifying fan blade damage during the engine’s operation using passive acoustic techniques. Signal processing and machine learning approaches were developed to process the acoustics data from a diffuse-field microphone. The laboratory tests have shown that the post-processing techniques can be used to detect and locate to the vicinity of a single fan blade and to characterise certain types and severity of damage.