A developed algorithm for the evaluation of calibration intervals for equipment devices in testing and calibration laboratories

Abstract In the present study, a method has been developed to evaluate the calibration interval for equipment devices based on their calibration records. Small-shift sensitive control charts along with a linear predictive model have been used to predict the date at which the performance of the equipment is expected to be out-of-tolerance. The Monte-Carlo algorithm has been implemented to simulate errors of random and systematic trend components. The responses of the cumulative sum (CUSUM), V-mask, and moving average (MA) control charts have been investigated and compared with the usual Shewhart control chart in various cases. The CUSUM control chart has shown an optimum performance in assessing the systematic trend and reasonable estimation for the calibration intervals. An oversensitivity to the systematic trend error, on the other hand, has been observed in the performance of the V-mask QC that may lead to unnecessary short calibration intervals. The MA control chart has shown a similar performance to the CUSUM. However, its parameter requires to be adjusted based on the size of the calibration records.