Improving of the Type A Uncertainty Evaluation by Refining the Measurement Data from a Priori Unknown Systematic Influences

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 267)

Abstract

A new approach to improving the type A uncertainty evaluation by cleaning of the collected data from unwanted influences which appears as non- periodical and periodical systematic components identified in the data is presented in the paper. The approach refers to regularly in time sampled data. The cleaning process comply with the main stream of ISO GUM recommendation and can be recognized as good practice in the proper estimation of the type A uncertainty. The proposed approach is discussed in the paper and the numerical example is presented as well.

Keywords

uncertainties data cleaning type A uncertainties 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Industrial Research Institute of Automation and Measurement PIAPWarsawPoland
  2. 2.Lodz University of TechnologyLodzPoland

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