Strain-Gage Balance Calibration Analysis Using Automatically Selected Math Models

A technique is presented that determines a set of candidate math models in order to analyze strain–gage balance calibration data using global regression. At first, a permitted and a required math model are defined in order to bound possible candidate math models. In the second step, starting with the permitted math model, families of possible candidate math models are created by generating all possible math term combinations for a fixed number of terms. Then, after the global regression has been applied to the calibration data using each family member, the next candidate math model is found by comparing the standard deviation of the response residual. This process is repeated for each gage using the updated candidate math model until only the required math model remains. A three component balance calibration data set is used to illustrate the determination of candidate math models. In addition, results from a six component balance calibration are discussed in order to show the application of the proposed method to realistic balance calibration data.