UNCERTAINTY ANALYSIS OF PRESSURE GAUGE CALIBRATION: COMPARISON OF THE GUM AND MONTE CARLO METHODS - Aidil Arief Ananta; Irsyadi Yani; Barlin (PT. Radja Intercontinental Publishing, 2026)
This paper presents a comparative uncertainty analysis of pressure gauge calibration using the Guide to the Expression of Uncertainty in Measurement (GUM) method and the Monte Carlo Simulation method. Pressure gauge calibration was conducted over a range of 0 to 25 bar to determine indication errors and their associated measurement uncertainties, which are vital for metrological traceability. The study evaluates several uncertainty contributors, including reference standard uncertainty, repeatability, resolution, zero deviation, and hysteresis. While the GUM approach utilizes a standard analytical framework based on linear approximations, the Monte Carlo method provides a probabilistic propagation of input distributions through 1,000,000 numerical iterations. The results demonstrate a high level of agreement between the two methods, with differences in expanded uncertainty remaining below 5% across the investigated range. Hysteresis and repeatability were identified as the dominant uncertainty components, particularly at higher pressure points. The findings confirm that the analytical GUM approach is reliable for this linear calibration model, while the Monte Carlo simulation serves as an effective complementary tool for validating complex or non-linear measurement problems.