Automating the Interpretation of Thermal Paints Applied to Gas Turbine Engines Using Raman Spectroscopy and Machine Learning

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Abstract

Thermal paints are paints that exhibit a number of permanent colour changes at various temperatures. Rolls-Royce, a producer of gas turbine engines, use thermal paints to map the surface heat distribution over components in gas turbine engines. Engine components are coated with thermal paints and built into engines. The engine is run which heats the components, and hence the paints. This results in a colour distribution over the surface of the painted components. This project aims to generate predictions for the temperature that the thermal paints applied to gas turbine engines have reached during engine operation. Training models are built using Raman spectra taken from known temperature paint samples. Raman spectra from the painted engine components are tested in these training models to generate temperature predictions. The known temperature paint samples are heated in an oven, while the paints applied to engine component are heated in a gas turbine engine. This leads to differences in the spectra of the known temperature paints and the engine run paints, complicating the training model.This thesis presents a method for classifying the spectra from the known temperature paints samples and the unknown temperature engine samples in such a way that meaningful predictive models can be built.

Keyword(s)

Machine learning; Raman; Thermal paints

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