https://www.mdu.se/

In condition monitoring, the reliability of a predictive maintenance program is critically dependent on the precision of data obtained from measurement systems. With increased availability, a significant challenge is evaluating the capability of these measurement systems to ensure data precision, which is fundamental for informed system selection. To address this challenge, this study proposes a systematic framework for evaluating the capability of these measurement systems using Gage repeatability and reproducibility (Gage R&R) technique, subsequently judging the acceptability level and guiding their selection to guarantee the data precision. Our study investigates the capability of these systems in terms of repeatability and reproducibility, quantifying the contributions of different sources to the systems' capability and providing directions for measurement system correction and enhancement. Another distinctive innovation of our approach is the use of three-region graphs, incorporating metrics including percentage of Gage R&R to total variation, precision-to-tolerance ratio, and signal-to-noise ratio, which presents a comprehensive overview of the systems' capability within one single figure. Two comparative experiments in distinct application scenarios were conducted to validate the effectiveness of the proposed framework. The insights presented serve as a valuable reference to replace the commonly used experience-based system selection in condition monitoring. Through this framework, we present a promising data-based approach aimed at enhancing the widely employed time-based calibration strategies, ultimately contributing to the improvement of data quality and the overall success of condition monitoring initiatives.