Temperature Correction Curve Reconstruction Algorithm for Digital Thermometer Based on Pseudo Inverse

Abstract

At present, high-precision digital thermometers based on industrial platinum resistance have become a popular research direction and are widely used in environmental monitoring, medical health, industrial automation and other fields. However, due to the influence of materials and manufacturing processes, the measurement accuracy is average. With the increase of service life, it is inevitable that the temperature measurement deviation will be caused by the drift of the resistance value. The temperature correction algorithm is an effective method to improve the measurement accuracy of digital thermometers. Traditional compensation function correction algorithms such as polynomial fitting and B-spline fitting have good correction effect, but the problems of resistance drift cannot be solved. The piecewise linear correction algorithm is simple and easy to be implemented, but it requires multi-point temperature measurement and the correction effect is general in the face of nonlinear changes of temperature correction curve, which limits its correction accuracy and universality. Therefore, we propose a temperature correction curve reconstruction algorithm based on the pseudo inverse method. Firstly, the reconstruction matrix is built by the original data and multiple characteristic temperature points. Then, the complete temperature correction curve is reconstructed by the characteristic temperature points to be reconstructed and the reconstruction matrix. Finally, the reconstructed temperature correction curve is automatically included in the sample database, which improves the diversity of samples and the correction accuracy of the algorithm. Experimental results showed that the proposed algorithm has a better correction effect in the face of nonlinear changes and drifts of temperature correction curves. And the proposed algorithm is less affected by the number of characteristic temperature points and the selection combination. The complete temperature correction curve is well reconstructed by collecting only 4 characteristic temperature points. Therefore, the proposed algorithm can provide effective support for improving the measurement accuracy of digital thermometer.