Stand On Evaluation Method Of Measurement Uncertainty Of Transducer

The measurement uncertainty evaluation methods available, including GUM method and Monte Carlo method, have been discussed in detail, especially their application fields, advantages and disadvantages; To compensate their deficiencies, the convolution approach is proposed to applied for measurement uncertainty evaluation,especially for that of transducers.After that, the sources of measurement uncertainty of transducers are analyzed detailedly, including the non-linearity, the hysteresis and the repeatability; and the effect of these sources on performance of transducers is also discussed; meanwhile some commonly-used calibration methods are also presented, which can be used to improve the accuracy of transducers.Based on the study above, the measurement uncertainty evaluation method on transducers is proposed on the basis of the principle of convolution; since if all the probability density functions of uncertainty sources are available, the probability density function of the combined distribution can be obtained via convolution approach; therefore the measurement uncertainty of the transducers could be managed to be determined if the confidence is specified. To achieve this purpose, the discrete convolution algorithm is adopted. With respect to this algorithm, each probability density function need to be discretized so as to obtain the corresponding sequence as input of the algorithm; moreover, the efficiency and quality of convolution could be improved by selecting circular convolution or piecewise convolution, depending on the length of input sequence; the algorithm is realized via MATLAB. If the confidence level is α specified, the value of confidence coefficient k can be determined with this algorithm; while the confidence coefficient k is determined by experience in GUM.To verity the effectiveness of the method above, the measurement uncertainty evaluation on a resistance strain sensor and an eddy current displacement sensor is carried out, whose results are compared with those of the GUM and MCM method. The results indicate that the convolution method is more accurate than the GUM evaluation methods and MCM method.