Diffuse Reflection Infrared Fourier Transform Spectroscopy and Partial Least Squares Regression Analysis for Temperature Prediction of Irreversible Thermochromic Paints. Issue 5 (May 2022)

• Record Type: Journal Article
• Authors: Kitagawa, Akiharu; Welsh, Calum; Mackilligin, Harry; Licence, Peter
• Is Part Of: Applied spectroscopy. Volume 76:Issue 5(2022)
• Page Start: 531
• Page End: 540
• Publication Date: 2022-05
• Subjects: Partial least squares -- PLS -- temperature prediction -- thermochromic -- diffuse reflection Fourier transform infrared spectroscopy -- DRIFTS; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/00037028211065759
• Languages: English
• ISSNs: 0003-7028

Abstract

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 °C and RMSECV of 13.0 °C using three latent variables. Graphical Abstract