Detection of Unexpected High Correlations between Balance Calibration Loads and Load Residuals

An algorithm was developed for the assessment of strain-gage balance calibration data that makes it possible to systematically investigate potential sources of unexpected high correlations between calibration load residuals and applied calibration loads. The algorithm investigates correlations on a load series by load series basis. The linear correlation coefficient is used to quantify the correlations. It is computed for all possible pairs of calibration load residuals and applied calibration loads that can be constructed for the given balance calibration data set. An unexpected high correlation between a load residual and a load is detected if three conditions are met: (i) the absolute value of the correlation coefficient of a residual/load pair exceeds 0.95; (ii) the maximum of the absolute values of the residuals of a load series exceeds 0.25 % of the load capacity; (iii) the load component of the load series is intentionally applied. Data from a baseline calibration of a six-component force balance is used to illustrate the application of the detection algorithm to a real-world data set. This analysis also showed that the detection algorithm can identify load alignment errors as long as repeat load series are contained in the balance calibration data set that do not suffer from load alignment problems.