Multispectral True Temperature Inversion Based on Multi-Objective Minimum Optimization Principle of Reference Temperature
ZHANG Fu-cai1, 2, TANG Wei1, SUN Xiao-gang2*
1. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
2. School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Abstract：Multispectral thermometry is a process of retrieving the true temperature of radiators by measuring the information of multispectral radiations and using related theories and algorithms. The solution of spectral emissivity is still the key and difficulty in multispectral thermometry. Theoretically, it is necessary to know enough spectral information to obtain the true temperature of the radiator. Considering that the spectral emissivity of actual radiators at different spectrum and temperatures are usually inconsistent, and the solution of spectral emissivity is an unavoidable problem in non-contact radiation temperature measurement, it is of great scientific significance and application value to carry out the research on the solution of multispectral emissivity and the inversion methods of true temperature. After decades of development, the solution spectral emissivity can be generalized into four types of models. One is the grey body hypothesis model, which considers that spectral emissivity is a constant or its change can be neglected in the process of temperature inversion; the other is the wavelength hypothesis model, which considers that there is a certain relationship between spectral emissivity and wavelength in the process of temperature inversion. Thirdly, the true temperature hypothesis model, which considers that there is a certain relationship between spectral emissivity and true temperature in the inversion process of the true temperature, and establishes a model between spectral emissivity and true temperature and realizes the inversion of true temperature with iteration method; Fourthly, the establishment of a neural network model, which achieve true temperature inversion by the neural learning network. Based on the uniqueness of true temperature and the analysis of different hypothetical models, thethesis tries to find a generaltrue temperature inversion method without the hypothesis of spectral emissivity model and carries out the research work with multispectral true temperature inversion method as the core. The paper summarizes the characteristics of traditional multispectral true temperature inversion theories and methods. In view of the complexity of selecting the spectral emissivity model in the existing multispectral true temperature inversion process, a true temperature inversion method based on the constrained optimization principle of single objective function minimization is proposed. This method does not need to assume the spectral emissivity model and convert the true temperature solution problem into an optimization problem to solve the minimum of the objective function. By using a blackbody furnace and adding a filter with known spectral emissivity at the output port of the blackbody furnace light source to simulate the radiation source, the true temperature inversion of multispectral pyrometer based on minimum optimization method is realized. Compared with the traditional second measurement method, under the same initial conditions and compared with the original second measurement method proposed by the research group, the new method has the same inversion accuracy as the second measurement method, but the inversion speed has been greatly improved.
Key words：Optimization; Multispectral; Emissivity; True temperature
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