Evaluation of Humidity Sensor Calibration Uncertainty by Monte Carlo Method

To effectively solve the problem that the measurement uncertainty evaluation result is not accurate and the calculation is complicated when the humidity measuring instrument is calibrated. The “Monte Carlo simulation method” (MCM) was proposed to evaluate the measurement uncertainty of humidity sensor calibration results. In this process, firstly, by analyzing the calibration process of humidity sensor, the measurement model that can accurately and completely reflect the actual measurement situation is constructed; then, design a performance testing method for the humidity generator to obtain parameter data that can truly reflect the performance of the current humidity generator; finally, taking the 55%RH calibration point as an example, by using the above measurement model and related parameters, single MCM method and adaptive MCM method were used to evaluate the measurement uncertainty of the humidity sensor calibration results. The evaluation results obtained are the same as: the best estimated value of humidity sensor measurement error ΔH = 0.01%RH, the standard uncertainty u(ΔH) = 0.14%RH, and the shortest coverage interval [ΔH_low, ΔH_high] = [− 0.24%RH, 0.26%RH] when the coverage probability is 95%. Through this application experiment on the MCM method, it was found that compared to the GUM method, the MCM method can effectively improve the credibility of the measurement uncertainty results of the humidity sensor. Moreover, when the adaptive MCM method is applied to evaluate the measurement uncertainty of the humidity sensor, compared to the single MCM method, it can effectively reduce simulation times, reduce storage space resources, and improve evaluation efficiency. Prioritizing the adaptive MCM method in practical operation is recommended.