1、 American National Standard Guide for Electromagnetic CompatibilityComputations and Treatment of Measurement Uncertainty Sponsored by the Accredited by the American National Standards Institute IEEE 3 Park Avenue New York, NY 10016-5997 USA 13 March 2013 Sponsored by the Accredited Standards Committ
2、ee C63 Electromagnetic Compatibility ANSI C63.23-2012C63American National Standard Guide for Electromagnetic CompatibilityComputations and Treatment of Measurement Uncertainty Accredited Standards Committee C63 Electromagnetic Compatibility accredited by the American National Standards Institute Sec
3、retariat Institute of Electrical and Electronics Engineers, Inc. Approved 3 December 2012 American National Standards Institute Abstract: Methods for estimating measurement uncertainty of emissions measurement results are provided, for use in conjunction with the basic methods of ANSI C63.4. Include
4、d in this document are both Type A and Type B uncertainty evaluation methods. Keywords: ANSI C63.23, electromagnetic compatibility (EMC), emissions, immunity, measurement uncertainty, metrology, Type A evaluation, Type B evaluation The Institute of Electrical and Electronics Engineers, Inc. 3 Park A
5、venue, New York, NY 10016-5997, USA Copyright 2013 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Published 13 March 2013. Printed in the United States of America. IEEE is a registered trademark in the U.S. Patent (978) 750-8400. Permission to photocopy portions
6、of any individual standard for educational classroom use can also be obtained through the Copyright Clearance Center. Errata Users are encouraged to check the IEEE Errata URL (http:/standards.ieee.org/findstds/errata/index.html), and the one for ASC C63at http:/www.c63.org/explanations_interpretatio
7、ns_request.htm, for errata periodically. Interpretations Current interpretations can be accessed at the following URLs: http:/www.c63.org/explanations_ interpretations_request.htm. For more information about the committee that produced and maintains this standard, visit the ANSI Accredited Standards
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20、nt of such rights, is entirely their own responsibility. Further information may be obtained from the IEEE Standards Association. vi Copyright 2013 IEEE. All rights reserved. Participants At the time this guide was published, Accredited Standards Committee C63Electromagnetic Compatibility had the fo
21、llowing membership: Daniel Hoolihan, Chair Jerry Ramie, Secretary Organization Represented Name of Representative AlcatelLucent Technologies . Dheena Moongilan Alliance for Telecommunications Industry Solutions (ATIS) .Mel Frerking James Turner (Alt.) American Council of Independent Laboratories (AC
22、IL) Harry Hodes . John Repella (Alt.) American Radio Relay League (ARRL) Edward F. Hare . Kermit Carlson (Alt.) Apple, Inc. . Fraidun Akhi Indrandil Sen (Alt.) AT uncertainty. NOTE 1 Measurement uncertainty includes components arising from systematic effects, such as components associated with corre
23、ctions and the assigned quantity values of measurement standards, as well as the definitional uncertainty. Sometimes estimated systematic effects are not corrected for, but instead, associated measurement uncertainty components are incorporated. NOTE 2 The parameter may be, for example, a standard d
24、eviation called standard measurement uncertainty (or a specified multiple of it), or the half-width of an interval, having a stated coverage probability. NOTE 3 Measurement uncertainty comprises, in general, many components. Some of these may be evaluated by Type A evaluation of measurement uncertai
25、nty from the statistical distribution of the quantity values from series of measurements and can be characterized by standard deviations. The other components, which may be evaluated by Type B evaluation of measurement uncertainty, can also be characterized by standard deviations, evaluated from pro
26、bability density functions based on experience or other information. NOTE 4 In general, for a given set of information, it is understood that the measurement uncertainty is associated with a stated quantity value attributed to the measurand. A modification of this value results in a modification of
27、the associated uncertainty (for example, different measurement uncertainty values for a radiated emissions measurement result may apply in different frequency ranges). ANSI C63.23-2012 American National Standard Guide for Electromagnetic CompatibilityComputations and Treatment of Measurement Uncerta
28、inty 5 Copyright 2013 IEEE. All rights reserved. NOTE 5 This definition of measurement uncertainty (i.e., as repeated from 2.26 of ISO/IEC Guide 99:2007 B17), and for situations where uncertainty components arising from sampling are not considered, corresponds to the term and definition for standard
29、s compliance uncertainty given in 3.1.16 of CISPR 16-4-1:2009 B7. 4. Basic concepts 4.1 Introduction to measurement uncertainty Measurement uncertainty is the best estimated quantity by which a measured value differs from the true value of a parameter under evaluation. Correction factors (or biases;
30、 see the definition in A.1.4) are typically used to improve correlation between measurement devices and systems and the reference quantity to which they have been calibrated. Additionally, a correction factor is always accompanied by a measurement uncertainty. The need for such factors is an indicat
31、ion that the true value cannot be directly and completely obtained from the instrumentation and with the method of measurement used. Correction factors determined during equipment calibration processes determine the bias to be applied to the measurement result, and they are subject to measurement un
32、certainty. Determining the operational characteristics of the EUT with respect to electromagnetic compatibility compliance requires both emission and immunity measurements. This guide, however, only focuses on emission measurement, which is covered in, for example, ANSI C63.4. The measurements or ev
33、aluations involve the use of various instrumentation and techniques, requiring operator interaction and decisions. The measurement (or evaluation) process produces both random and systematic effects that influence the ultimate outcome of the measurement. Examples of systematic effects are bias of th
34、e measurement result by cable loss, parallax when reading a DArsonval meter, or habits and preferences of the operator. An example of a random effect is the influence of noise contribution to the measured amplitude if the amplitude of the measurand is close to the system noise floor. Additional pape
35、rs on the treatment of uncertainty components (influence quantities) are listed in an informative bibliography (i.e., see Annex B). An evaluation of each effect included in a measurement enables the identification of influence factors (i.e., elements that contribute to the uncertainty of the measure
36、ment), and a thorough description of each effect can assist in assignment of an appropriate value and weighting factor indicating how the overall measurement uncertainty budget may be affected. Each element of the measurement process involves an uncertainty of measurement that can be assigned a valu
37、e and described by the owner of the process. The combinations of the elements of uncertainty form the basis of an uncertainty budget for a particular measurement process. 4.2 Concepts of uncertainty 4.2.1 General Measurement uncertainty is a parameter associated with the result of a measurement that
38、 characterizes the dispersion of the values that could reasonably be attributed to the true value of the measurand. It shows the spread of values above and below the measurement result within which the true value of the measurand may be expected to lie. It can be considered to be a measure of the po
39、ssible error in the value of the measurand provided by the result of a measurement; however, the true value of a measurand can never be known. Measurement uncertainty should not be confused with correction or correlation factors. Correction and correlation factors are quantities added to measurement
40、 values to create agreement between established standard values and measurement device readings (these can also be described as biases: see A.1.4). ANSI C63.23-2012 American National Standard Guide for Electromagnetic CompatibilityComputations and Treatment of Measurement Uncertainty 6 Copyright 201
41、3 IEEE. All rights reserved. Two types of evaluations are defined to determine the values for an uncertainty budget: Type A and Type B. The type of evaluation used depends on what input and reference data is available. Other electromagnetic compatibility (EMC) measurement uncertainty documents, such
42、 as CISPR 16-4-2 (an international standard) and UKAS LAB 34:2002 B21 (a UKAS guideline document), describe primarily the Type B analyses and may use Type A methods to obtain only a few contributors, whereas Clause 5 of this guide describes how to apply either method. 4.2.2 Type A evaluation Type A
43、evaluations of uncertainty are those obtained by using statistical methods where multiple observations of the same event are recorded. These observed values are used to calculate the standard deviation of the results. The standard deviation is then used to obtain the contribution of the process unde
44、r observation to the uncertainty budget. 4.2.3 Type B evaluation Type B evaluations of uncertainty are those by any method other than a statistical evaluation. Knowledge of previous performance of instruments, specifications, instrumentation reference data, and uncertainty data provided with calibra
45、tions are examples of this evaluation type. 4.2.4 Type A or Type B evaluation Either a Type A or a Type B analysis shall be carried out for each contributor to the uncertainty budget. A Type A analysis has the advantage of providing a more representative evaluation because it is based on the actual
46、test setup/equipment used during measurement and, as such, generally results in a smaller contributor value than a Type B analysis. However, a Type A analysis is more demanding in terms of time and resources because it usually involves testing or analyzing extensive amounts of data. A Type A evaluat
47、ion of a contributor is determined by following the guidance for that particular contributor (see 5.6) and finding the standard deviation for the data collected. For example, antenna factor accuracy (also called antenna factor calibration) can be determined by taking the calibration value at each fr
48、equency over the past five calibrations and determining the worst-case standard deviation over the antennas usable frequency range. This could result in a smaller contributor than the Type B contributor. If the Type B contributor for antenna factor accuracy was 0.6 dB, and the Type A analysis of the
49、 data yielded a contributor of 0.2 dB, then it would be advantageous to use the smaller contributor. 4.2.5 Measurement uncertainty budget calculation Annex A includes a detailed presentation of measurement uncertainty concepts and methods of calculation of contributors contribution to the measurement uncertainty budget based on their known or estimated probability distribution. Table 1 summarizes the formulas to be used for each type of probability distribution and provides cross-references to corresponding subclauses from Annex A. ANSI C63