Session: 36-09 Uncertainty Quantification & Sensitivity Analysis (1)

Paper Number: 125684

125684 - Beyond the Error: Unveiling Uncertainties in Pressure Sensor Measurements 

In every sensor lies inherent uncertainty. It's crucial to distinguish between error, accuracy, and uncertainty. Error reflects the discrepancy between an exact and measured value, a difference often elusive due to the inaccessibility of the 'exact' value. Uncertainty denotes the range of plausible values attributed to a measurement.

For accuracy, understanding the nature of uncertainty and defining the confidence interval is essential. Statistically, broader confidence intervals imply greater uncertainty. For instance, 68.3%, 95.4%, and 99.7% confidence intervals are typically represented by σ1, σ2, and σ3, respectively. Many datasheets cite accuracy in terms of average values, with a variation roughly equivalent to σ1.

Calibrating sensors involves many components, including reference pressure and temperature sensors, power supplies, interface cards, and acquisition equipment. Each has its inherent uncertainties which cumulatively influence the tested sensor.

There are several methodologies to evaluate uncertainties. Primarily, two main methods can be distinguished:

·       Type A: This approach focuses on repetitive measurements of a single component. It is thorough and often yields more precise uncertainties specific to that element. However, its detailed nature can be time-consuming.

·       Type B: This method leans on datasheets provided by manufacturers. It's faster, but can lead to broader uncertainties, especially if based on generalizations drawn from large component batches. When manufacturer data is lacking or insufficient, resorting to Type A becomes necessary.

Regarding pressure sensors, their operation is defined by a linear equation linking the applied pressure with the output and supply voltage. Using a model to represent this relationship inherently introduces another layer of uncertainty. It's worth noting that no model can perfectly match every data point, hence certain deviations will always exist. The objective is to determine the sensor's transfer function using the least squares method, which provides a clear understanding of its performance under varying conditions.

What Participants Will Learn: In the paper and during presentation, attendees will acquire the knowledge and skills to:

Perform uncertainty calculations with confidence.

Identify the primary sources of errors and grasp their origins.

Implement strategies to mitigate or avoid these errors, ensuring accurate measurements.

Conclusion: Uncertainty permeates every facet of sensor calibration, from test bench to the sensor itself including the way to mechanically implement it. Thus, ascertaining the uncertainty of influential components, using either Type A or B methods, is paramount. Moreover, evaluating the sensor's linear model and the model's uncertainty is essential. Predominantly, uncertainties stem from elements external to the primary sensor, especially the acquisition system. Therefore, combining uncertainties from both external elements and the model yields the sensor's overarching calibration uncertainty.

Presenting Author: Patrick Hendrick University of Brussels

Presenting Author Biography: 2004 - Present: Professor @ University of Brussels
2007 - Present: Professor @ KU Leuven
2004 - Present: Invited Professor @ Royal Military Academy
1994 - 2004 : Professor @ Royal Military Academy
1993 - 2004 : Lecturer @ Royal Military Academy, UAV Center

Authors:

Michel Saint-Mard SENSORADE SA
Alexis Kozlowski SENSORADE
Adrien Hertay Sensorade
Patrick Hendrick University of Brussels