A novel soft intelligent calibrator for low-velocity hot bulb anemometers

The low-velocity hot bulb anemometers is a type of thermal wind velocity sensor and they are widely used in various fields, the accuracy and credibility of their measurements are of great importance for their applications, therefore, accurate calibration is an effective guarantee for the measure accuracy. Recently, for low-velocity hot bulb anemometers, most relevant literatures have focused on the calibrations through parameters modification implanted in the hardware during the production process, and the detailed measurement principle of the hot bulb anemometers is needful to execute the parameters modification work, which restricted their application. This paper proposed a soft intelligent calibrator for low-velocity hot bulb anemometers via Polynomial fit method and back-propagation neural networks, and the wind velocity calibration principle of the hot bulb anemometers was given with the aid of the sample data. Two hot bulb anemometers with different pre-calibration relative characteristics were calibrated, the measure precision of the wind velocity were improved, and the post-calibration relative errors were less than the pre-calibration relative errors. This soft calibrator provides ample evidence for its effectiveness of calibration performance for the hot bulb anemometers, which could also be applied for calibrating sensors in other fields.