INCOLAB – DK activities

SC 4 meeting

Borås, Sweden

14 June 2004

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INCOLAB - DK

• The LVD organisation in DK

Ministry of Industry and Economy

Ministry of Science,Techn. and Education

AuthorisationBased on accreditation

Notified Body:Electricity Council

Accr. body:DANAK

Market surveillance

Accr. test lab:Demko-UL

NMI:DANIAmet

Accr. cal labs:Arepa

Accr. test (IT):Delta

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Activities – January-June 2004

• WP0:SC4 Meeting, June

• WP1:DEMKO comparison measurements in progress as we speak

• WP4:Guideline development

writing in progressdiscussions with Dutch partner

• Costs this half-year: 55 man-hours, incl. SC4

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Economic status

• Costs so far:

Cost catagory Budget Year 1 %Personnel costs 77220 28149 36%

Travel & Subsisten. 25500 3683 14%ComputingSubcontracting 5000 841 17%Other costs 0 321Overhead 20544 6431 31%Total 128264 39425 31%

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WP4 - Guide

• ContentIntroduction – including the discussion of the inherent “method uncertainty” non-uniqueness of standards

The “Why uncertainty?” question

Basic GUM philosophy and practice

Model-building

Generic models for basic measurements

Uncertainty of a calibration curve

Statistical treatment of test data and their relation to GUM

Documentation of uncertainty budgets

Examples

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Uncertainty in testing

• Aim of the work: To put together a guide for practical uncertainty estimation in testing

• Several documents exists (EA, A2LA, …), but they never seem to ”get down to business”

• Quick to point to the general obstacle: Standards for testing allow the implementation of tests to vary to such a degree, that ”method uncertainties” dominate – direct comparability of results is difficult

• Also “GUM is difficult, so why bother…”

• Many rely only on repeatability and reproducibility

• and biases revealed from comparisons…

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Beware!

• All repeatability and reproducibility studies only give information on random effects! Hence are information of the measurement process.

• Only a detailed (physical) examination of the measurand and measurement method may uncover sources for systematic effects.

Also

• Uncertainty relates to a measurement result, and a quantative test result should be treated as such.

• One requirement is a well-defined measurand (well-defined test conditions) … and this often not given in a test standard.

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Technical contents

• Summary GUM method

• Main difficulty of users is setting up the appropriate model function (relation between input quantities and the output quantity(ies))

• Hence, present a ”toolbox” of generic measurement methods:

Direct reading from a calibrated instrument

Substitution/transfer measurements

Balance/difference measurements, …

and the often usedGeneration and use of calibration curves

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Calibration curves

• Given calibration data: applied stimuli {xi , u(xi)} and measured responses {yi , u(yi)} and an empiric or physical bond f(x,y) = 0

• Find for a measured response y with u(y), the corresponding stimuli x and associated u(x).

x

y

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Trueness and precision

• On statistical treatment of test series trails:

• Precision ~ repeatability and reproducibilityA measure for the ability of the lab to generate consistent results

Can be estimated from internal sources

• Trueness ~ biasA measure for the ability of the lab to generate correct results

Must be estimated from external sources (e.g. reference materials)

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Practical guide

• Documentation; especially important for comparability

The better the measurand is specified, the higher the comparability between results.The assumptions of the measurement model and the uncertainty contributions are the key, and should be detailed.

• And examples …Heating in Black Test CornerGlow wireEMC…