Low-Cost Pyrometry System With Nonlinear Multisense Partial Least Squares

Accurate high-temperature measurement is very important for process monitoring of an industrial system. Infrared thermometers usually can handle no more than 1000 °C and should use some expensive accessories for higher temperature measurements. This paper proposes a low-cost pyrometry system with nonlinear multisense partial least squares (NMSPLS). The ordinary camera with different filters is designed to collect the images of hot object at different wavelengths, and the NMSPLS is presented for predicting the temperature of the hot object from the obtained images. For the proposed method, the obtained images are represented by the multisense tensor, where red, green, and blue are regarded as three different dimensions in a sense of the tensor, respectively. The proposed method integrates an outer model and a nonlinear inner model. For the outer model, the independent variables and the dependent variables are projected into a low-dimensional common latent subspace. The weight matrices are calculated from the independent variables by the tucker decomposition, and the single value decomposition is adopted for extracting the latent variables (Lvs) based on the covariance between the independent variables and the dependent variables. For the nonlinear inner model, the neural network is adopted and the extracted Lvs are used as the input and the output of the neural network, respectively. Two real experiments are performed for estimating the proposed method. The experimental results verify that the proposed method can be applied for pyrometry and have higher effectiveness.