Fdstnet & TransCAM: A 1D CNN temperature interpretation model for temperature indication paint and model interpretability method

Abstract

The color of temperature indicating paint (TIP) is commonly used in temperature measurement testing of aircraft engine hot end components. In this paper, an end-to-end convolutional neural network utilizing DSTNet, is proposed for accurate temperature interpretation of TIP. Furthermore, the DSTNet is optimized by fusion of reflectance spectrum and sub-band statistical features, resulting in the creation of FDSTNet. The FDSTNet achieves highly accurate temperature interpretation. When the temperature error is 0℃, the interpretation accuracy on KN3A, KN6 and KN8 samples is 98 %, 97 % and 94 %, respectively. When the temperature error is ±10℃, the accuracy on the engine blade is 88 %. In addition, a 1D convolutional neural network interpretability method based on TransCAM, is proposed. This approach analyzes the process of learning the reflectance spectrum of DSTNet, and localizes the characteristic wavelength by visualizing the class activation mapping curves of each convolutional layer of DSTNet.