Diffuse Reflection Infrared Fourier Transform Spectroscopy and Partial Least Squares Regression Analysis for Temperature Prediction of Irreversible Thermochromic Paints

Temperature measurement of internal components of a jet engine is a crucial control parameter to ensure its component life and efficiency. Particularly for thermal analysis of internal components of jet engines, irreversible thermochromic paints (TPs) have been developed at Rolls-Royce plc to evaluate the surface temperature of engine components where it is otherwise impossible. Thermochromic paints change color with respect to an increased temperature whereby the resulting change in the TP color corresponds to the maximum temperature experienced by the surface of engine components during testing. To improve the reliability and reproducibility of the temperature measurement by TPs, this work explored the potential use of diffuse reflection Fourier transform infrared spectroscopy (DRIFTS) combined with partial least squares regression (PLSR) analysis. The outcome of the prediction of the raw and pre-processed datasets was compared and discussed. The major contributors to the prediction models were the change in the property of the surface M-OH bonds, the structural change of the inorganic pigments and fillers, and their solid-state reaction at a higher temperature. The result showed improved reliability of the prediction model after the combined pre-process treatments with reported RMSEC of 4.5 degrees C and RMSECV of 13.0 degrees C using three latent variables.

Automated summary

Temperature measurement is crucial for the life and efficiency of internal components in a jet engine. Rolls-Royce developed irreversible thermochromic paints to evaluate the surface temperature of engine components that cannot be directly measured. This study explored the use of diffuse reflection Fourier transform infrared spectroscopy and partial least squares regression analysis to improve the reliability of temperature measurement by thermochromic paints. The results showed an improved reliability of the prediction model after the combined pre-process treatments.