Application of Global Regression to Single-Piece Balance Calibration Data

The application of global regression during the generation of the load prediction equations from calibration data of NASA's ARC7K six-component single-piece balance is discussed. The calibration data was obtained by using NASA Langley's 9-point load schedule design. Past experience showed that the successful application of global regression to data from Langley's 9-point design must satisfy two requirements. First, a tare load iteration must be performed during the calibration data analysis that uses the mean zero load outputs of each load series as the electrical description of the tare loads. In addition, a weighted least squares fit must be applied so that data points of single-component load series have more influence during the determination of the load prediction equations. An improved weighting scheme was recently developed for this purpose. Calibration and check load data of the ARC7K balance were processed with both the load prediction equations of the Iterative and Non-Iterative Method to demonstrate and evaluate the application of global regression to data from Langley's 9-point load schedule design.