Identifying flow-sensor gain and offset with timed transfers
ID: identifying-flow-sensor-gain-and-offset-with-timed-transfers
Identifying flow-sensor gain and offset with timed transfers uses a known gas volume delivered over different durations to distinguish multiplicative response from accumulated zero error. In the model , a transfer of volume lasting producesThe observations form a linear regression with design matrix rows . If , gain and offset are identifiable only when the durations are not all equal. Repeating one duration measures the same combination of and more precisely, without separating them.
For two noise-free transfers,Stipulated 3 L transfers lasting 1 and 6 s, measured as 3.020 and 3.120 L, give and . The different durations expose an offset that one transfer could misinterpret as a gain error.
Timing spread and gain-offset estimator covariance quantifies the design. Suppose observations have independent equal volume-error variance , and define . Ordinary least squares givesUnder the stipulated model, wider timing separation improves offset precision. For the two durations above and L, the offset's standard uncertainty is about and the gain's about 0.00406.
The reference syringe volume was treated as known in those formulas. If it is uncertain, repeated sensor tests share that reference uncertainty. Indeed the observations identify only the product when both factors are unknown. Additional repetitions cannot establish an absolute gain without an independently characterized volume. The GUM treats fit uncertainties and common-reference correlations as distinct contributions.[ref]
Guide to the expression of uncertainty in measurement by JCGM. §4.2.5, fitted parameters; §5.2.4, common reference data
The primary spirometry statement's multiple-flow syringe checks test the practical response over different delivery rates.[ref]
Standardization of Spirometry 2019 Update by Brian L. Graham and others. Calibration and Quality Control, 3-L syringe over multiple flows
Actual leaks, flow-dependent nonlinearities and uncertain timing require a richer model than a constant gain and offset. New to topics? Read the docs here!