Prediction of the optimum instrument calibration interval using grey-based second-degree polynomial model

Several grey-based stochastic models are applied to predict the optimal calibration interval in a measuring instrument. On the basis of a grey calamity prediction method, the original data are preprocessed such that they become monotone-increasing. Subsequently, GM(1,1), exponential fit, linear fit, and general polynomial fit are respectively integrated into the stochastic model to verify the capability of prediction. By using the LabVIEW programming language, the effectiveness of each developed model has been evaluated through the actual data which was collected in SJSMIT calibration laboratory. Results demonstrate that the grey-based second-degree polynomial model is the best one for the swung serial data