Dynamic Outlier Detection in the Calibration by Comparison Method Applied to Strain Gauge Weight Sensors

The present work proposes a robust method of analyzing sets of data series shifted in time in respect to each other utilizing the process of dynamic calibration by comparison. Usually the Pearson's correlation analysis coefficient is applied for this purpose. However, in some cases the method does not bring satisfactory results, as it can be seen in the results of the research conducted for the purpose of this paper. The Dynamic Time Warping method may be the solution to this problem, as it appears to be more efficient while comparing the shapes of calibration characteristics done with the use of the Pearson's method. The presented method may also be applied to eliminate dynamic outliers collected in the process of recurrence examination or the analysis of strain gauge weight sensors hysteresis. This fact also makes the method a good tool for eliminating improper data series which might appear in the calibration process due to, e.g., malfunctioning devices installed in the calibration stand. The article presents an example of using the proposed method in eliminating improper dynamic characteristics obtained in a simulated calibration stand. Moreover, a comparative analysis performed on the simulation data is also presented in the article, as well as the result of the laboratory experiment.