noise and fsv

8/6/2019 Noise and FSV

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Published in IET Science, Measurement & Technology

Received on 26th July 2007

Revised on 1st October 2007

doi: 10.1049/iet-smt:20070063

ISSN 1751-8822

Modifying the feature-selective validationmethod to validate noisy data setsJ. Knockaert

1 J. Peuteman

2 J. Catrysse

2R. Belmans

1

1Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Onderzoeksgroep ELECTA, Kasteelpark

Arenberg 10, Heverlee B-3001, Belgium2Department of IW&T, Katholieke Hogeschool Brugge Oostende, Zeedijk 101, Oostende B-8400, Belgium

E-mail: [email protected]

Abstract: Objective validation and ranking of measurements and simulations may be done by methods such as

feature selective validation (FSV). FSV is used to compare two EMC-measurement results. Owing to

the noisy nature of these type of data, the FSV results are corrupted. The reasons are discussed and

solutions are proposed to make FSV feasible in a broader area of applications. The final solution is a

combination of denoising the data and changing the weight of the data to be in accordance with our visual

interpretation.

1 IntroductionTo put computational electromagnetics (CEM)validation methods in a wider context, someexplanations on the search for CEM standardisationare needed. Section 2 places feature-selectivevalidation (FSV) method and integrated error againstlog frequency (IELF) in the area of standardisation.Some remarks on this history are necessary tounderstand the main purpose of FSV and whymodifications are needed if FSV is to be used for otherpurposes. Section 3 starts with a discussion on

detectors. The width of the noise band is estimatedfor peak and quasi-peak measurements. The rest ofthis section discusses why noise corrupts the FSVresult. Three possible solutions are investigated in theSection 4. One solution changes FSV, and two othersolutions reduce the noise. The Section 5 combinesthe ideas of both the modified FSV and IELF. The lastsection discusses a real application where the modifiedFSV versions were used.

2 Validation

2.1 Background

For simulation of electromagnetics, engineers andscientists have to choose from a vast amount of CEM

methods. Examples are finite element method (FEM),method of moments (MoM), finite-difference timedomain and so on. As these methods are numerical,discretisation both in space and time is used and asimplification of a complex reality is needed.Therefore all methods may give different results. Thisraises the question which method is correct or gives atleast the best approximation. This unansweredquestion resulted in the start of the IEEE standardproject P1597.1 in 2001 Standard for validation ofcomputational electromagnetics (CEM) computermodelling and simulation [1].

Two key areas for benchmarking can be distinguished.The first area is the validation by canonical models. Thisinvestigation results in a set of standard EMC problemsusable to evaluate the modelling tools [2]. The secondarea is the validation by simulation againstmeasurement. Validation methods such as FSV andIELF are in this area.

2.2 Need for objective validation

While presenting a paper on an EMC-symposium, the25 participants were asked to evaluate 5 EMC results

from different measuring setups (Mini-symposiumEMC 2007, 5th April 2007, Hogeschool vanAmsterdam, organised by the Dutch EMC-ESD

/ IET Sci. Meas. Technol., 2008, Vol. 2, No. 4, pp. 244257 & The Institution of Engineering and Technology 2008doi: 10.1049/iet-smt:20070063

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organisation). Fifteen of them did fill in the form. Thefive spectra can be found in the example at the end ofthe paper. The participants had to give a ranking (15), to express the best to worst correlation. Fig. 1shows a histogram of the votes. For the LISNVP (lineimpedance stabilisation network used as voltage probe,see Section 6.2.1.), six people found this the bestcomparison, four people the second best and so on.

Some statistics are shown in Table 1. The meanvalue for the LISNVP is 2.00 with a standarddeviation of 1.00, thereby reaching the second placein the ranking. Some conclusions can be drawn.There is no consensus at all. The largest consensus(smallest standard deviation) is noted on the finallybest (LISNVP) and worst (EFT, electrical fasttransients) ranked method. This is only aconfirmation of what statisticians have already proved

for decades.

This small inquiry shows that a validation ofmeasurement data is very subjective and may lead tohuge discussions. An objective validation can avoidsubjective discussions.

2.3 Feature-selective Validation

FSV is a method for validation of CEM [3], withapplications in EMC and signal integrity. This methodhas shown its usefulness in the validation of EMC

models [3].

When comparing two data sets, normallymeasurements and simulations, FSV decomposes bothdata sets into two parts, trend and feature data. Thetrend data can be seen as the low frequency part,whereas the feature data or fast variations can be seenas the high-frequency part. The terms low and highfrequency can be confusing when the data sets

represent a measurement in the frequency domain.The low and high-frequency parts have nothing to dowith the frequency axis, but with slow or fastvariation of the shape of data set itself. Analysing the

low-frequency part gives a measure of similarity ofthe trend (ADM or amplitude difference measure).Analysing the high-frequency part of both data setsgives a measure of the similarity of the feature (FDMor feature difference measure). These figures combineto a global goodness-of-fit value (GDM or globaldifference measure). The strength of the FSV-methodis the point-by-point comparison showing at whichdata points the comparison fails. Combination of allmeasures to one figure, expressed by a naturallanguage description (excellent up to very poor), isa further strength.

One of the weak points of FSV is the choice of theboundary between low- and high-frequency parts. Theseparation point is chosen empirically. According to asensitivity analysis, this boundary is considered asrather good [4]. The actual problem is that theboundary is possibly not optimal for comparison ofother typical data sets, like two EMC peakmeasurements. The boundary is considered as one ofthe future research points [4].

In this paper, FSV is used to compare the conductedemission measurements. EMC measurements, as shown

in Fig. 2, are characterised by noisy data, especiallywhen using a peak and average detector.

2.4 Integrated error against logfrequency

Consider both measurements in Figs. 2a and 2b. Whenanalysing the correlation of both curves, it is unclearwhich one has the best value. In reality, there is nodifference between the top and bottom one.The measurements are the same, but on the top figurethe frequency axis is linear, on the bottom one, the

frequency axis is logarithmic. EMC measurementsare measured by linear steps, but are visualisedlogarithmically.Figure 1 Histograms of the votes

Table 1 Mean value, standard deviation and

ranking according to the participants

Mean Standard deviation Rank

LISNVP 2.00 1.00 2

VP 4.00 1.00 4

CVP 1.67 0.82 1

EFT 4.20 0.86 5

CFP 3.13 1.30 3

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To compare data sets, statistical methods can beused. However, with linear data, logarithmically

visualised, 50% of the data is compressed in 15% ofthe picture. Normal statistic methods such as aPearsons correlation put the same weight on alldata points, and by visual inspection, there is moreweight on the first (left) part of the data points.Visual inspection emphasises the left part of the datapoints.

IELF was developed to cope with this problem [5]. Itputs more weight on the left part of the data set anddecreases this weight logarithmically. Evaluating thedifference on a logarithmic frequency base increases

the accordance with the visual interpretation. It isshown that this evaluation gives better results whenranking various measurements [5]. The basic IELF

equation (1)

IELF

Pn1i1

jerrorij:{ l n ( fi1) ln ( fi1)}=2ln(f

n)

ln(f0)

(1)

has been modified to (2) to provide better results

IELFmod

Pn1i1 jerrorij:{ln(( fi1 fi)=2)

ln(( fi fi1)=2)}ln ( fn) ln ( f0)

(2)

The problem of the IELF equations mentioned is that thedata is not normalised: data sets with a different meanvalue are not ranked correctly [6]. A very simplenormalisation gives good results. An example of atwo-step normalisation is:

make the mean value 0,

make the standard deviation 1.

3 Noisy data sets and FSV

3.1 Detectors

EMC measurements are performed by a

superheterodyne receiver (spectrum analyzer ormeasuring receiver). Four types of detectors arecommonly used: PK (peak), AV (average), QP (quasi-peak) and RMS. The basic detector for emissionmeasurements is the quasi-peak detector. Althoughit is difficult to analyse the quasi-peak detectortheoretically, for noise measurements, some interestingproperties have been illustrated. The ratio betweenquasi-peak and RMS value for a zero-mean Gaussiannoise or normally distributed (ND) noise input isapproximately 1.85 [7]. For the same type of input,the ratio between peak and RMS value is not known,but as a rule of thumb, 4 can be taken with high

confidence [7]. Combining both values gives thefollowing rule of thumb: the ratio between peak andquasi-peak value and the RMS for ND noise input canbe taken as 2.16. This means that the results of quasi-peak measurements are 6.7 dB lower than those of thepeak measurements in the case of ND noise or, inother words, the noise band is 6.7 dB smaller. Thisproperty is used in Section 4.3.

When performing emission measurements with apeak detector, the result is a trend line with a noiseband and particular features, such as small and broad

peaks. The noise band is typical ND noise as seen inSection 4.2. For the quasi-peak measurement, thenoise band is much smaller. Peak and average

Figure 2 Two peak measurements

a Linear axisb Logarithmic axis


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measurements are much noisier than quasi-peakmeasurement.

3.2 FSV and noisy data sets

As can be seen from Fig. 2, conducted EMI peakmeasurements are very noisy. A simple test canshow that this noise corrupts the FSV results. Thedevice under test is a PWM frequency inverter(three-phase, 400 V, 5.5 kVA) supplying aninduction motor. The measurements are doneconforming to the standards, using an LISN and testreceiver, in the range of 150 kHz to 30 MHz. Sixmeasurements are performed, twice a peak, averageand quasi-peak measurement. Equal measurementsare done directly after each other, without changing

disturbing factors in the surrounding area. Thismeans that both peak measurements should bealmost identical. The same can be said for theaverage and quasi-peak measurements. The threemeasurements are shown in Figs. 3a, 4a and 5a.

The black line on each figure shows the differencebetween both measurements.

Figs. 3b, 4b and 5b show the FSV results. The left partshows the probability density function of ADM, the

right part FDM. On the figures, also an overall ADMand FDM value is given.

As an additional test, two types of noise aregenerated. The first type is white noise, uniformlydistributed (UD) noise. The second type is ND whitenoise. Two data sets of UD noise are generated andvalidated by FSV (Fig. 6), the same is done for twotypes of ND noise (Fig. 7).

Table 2 shows the correlation and results of FSV. Thefirst row provides a comparison of identical data to havean idea of the best possible case. Comparing the results,the following things can be decided. Correlation has nomeaning in this comparison. ADM gives a valuableresult, whereas FDM only gives a valuable result onthe quasi-peak measurement. FDM makes nodifference between PK, AV and both noise types.

Figure 3 Peak measurements

a Two peak measurements (dark grey, light grey) and thedifference (black)b Validation of Fig. 3a (by FSV 3.2)

Figure 4 Average measurements

a Two average measurements (dark grey, light grey) and thedifference (black)b Validation of Fig. 4a (by FSV 3.2)


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FDM in Figs. 3b, 4b and 6 is very similar. GDM iscalculated by a simple quadratic mean formula

GDMi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ADM2i FDM2ip

(3)

A property of this formula is the emphasis on the largestvalue. Because FDM is corrupted by the noise, GDM isalso overestimated.

4 Modification of FSV

4.1 Weighted values

The peak and average measurements contain a lot ofhigh-frequency components which cannot be seen as afeature but act as noise. The point-by-point GDM iscalculated by (3).

In comparing conducted emission measurements, agood (small) ADM is combined with a bad (large)FDM. Using (3), the largest value (FDM) isemphasised. In noisy data, the ADM part is almostneglected in the GDM result. Only using ADM whenevaluating and neglecting FDM is not an option, as thefeature cannot be neglected.

To solve this problem, (3) can be changed.

GDMi mod 1

2: 2 ADMi FDMi

1(2=ADMi) (1=FDMi)

(4)

Equation (4) emphasises on ADM, while FDM is notneglected. The results of the classic FSV incomparison to the weighted FSV are given in Table 3.

This basic idea has been used in FSV 3.2 [8]. Theprinciple of weighing is more generalised and the

Figure 5 Quasi-peak measurement

a Two quasi-peak measurements (dark grey, light grey) and thedifference (black)b Validation of Fig. 5a (by FSV 3.2)

Figure 6 Validation of UD noise (by FSV 3.2)

Figure 7 Validation of ND noise (by FSV 3.2)

Table 2 FSV-results of the measurements

Comparison (Fig.) Corr% ADM FDM GDM

identical 100 0 0 0

peak (3a) 97.53 0.104 0.430 0.461

average (4a) 98.83 0.067 0.398 0.414

quasi-peak (5a) 99.83 0.031 0.150 0.159

UD noise2

0.42 0.401 0.435 0.658

ND noise 20.0034 0.415 0.454 0.687


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weighting factors are calculated objectively, according tothe grade and the spread of the FSV results.

4.2 Preprocessing the dataPreprocessing the data is a possible method to eliminatenoise, while keeping the feature. Nevertheless,eliminating noise and keeping the feature is not fullypossible, as this is even by visual inspection a difficultissue. Data sets under consideration are one with onlya trend and noise, but no feature (Fig. 8a) and onewith a trend, noise and an obvious feature (Fig. 9a).

The validation of the data shown in Fig. 9a by FSVgives better FDM results. This means that if datacontain more pronounced feature, FSV recognises it in

a better way and quantifies the results. A conclusion isthat eliminating the noise improves the validation

obviously. A way to do this is by detrending the dataand using a histogram on the noise.

For detrending the data, a simple moving average

algorithm (SMA) can be used. Other moving averagealgorithms, such as weighted moving average andexponential moving average, show no obvious benefit.EMC measurements are expressed in dB mV. For acorrect moving average of the measured data, the datashould be expressed linearly first. Nevertheless,applying an SMA to logarithmic instead of linear datais the same as applying a geometric mean instead ofan arithmetic mean, as proven in (5) and (6) for ann-point moving average

MAdBP

ni1 Ai dB

n Pni1 20 log10 (Ai)

n

20 log10

ffiffiffiffiffiffiffiffiffiffiffiYni1

Ain

s (5)

MA 10MAdB=20 ffiffiffiffiffiffiffiffiffiffiffiYn

i1Ain

s(6)

To approximate a featureless trend, a 100-point SMAgives good results for the approximately 6000-pointdata set. Figs. 10 and 11 show the the trend data of

Figure 8 Data set with no feature and its validationa Data with trendline and noise, no particular featureb Validation of the data set in Fig. 8a (by FSV 3.0)

Figure 9 Data set with obvious feature and its validation

a Data with trendline, noise and obvious featureb Validation of the data set in Fig. 9a (by FSV 3.0)

Table 3 Results of GDM and GDMmod(FSV 3.0)

Comparison (Fig.) GDM GDM mod

identical 0 0 (limit)

peak (3a) 0.461 0.339

average (4a) 0.414 0.280

quasipeak (5a) 0.159 0.111

UD noise 0.658 0.671

ND noise 0.687 0.696


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both data sets under consideration, Figs. 12 and 13 thedetrended and Figs. 14 and 15 show the histogram ofthis detrended data sets.

Visual inspection of the first histogram (Fig. 14)learns that the detrended data of the left data set

resembles the histogram of ND noise. These data canbe seen as a trend line with noise and no particularfeature. To eliminate noise, the left and rightboundaries have to be chosen wherein all data can beeliminated. For the histogram of the second data set(Fig. 15), it is far more difficult to choose boundaries.It is obvious that the upper boundary can be seen.The noise goes approximately up to bin 10 or 12. Thereal peaks give values between 15 and 43. For thelower boundary, we can see two Gaussian curves. It isdifficult to say what boundary to choose, 210 or220. For EMC measurements, the problems arerelated to the upper parts. The boundary of the lowerparts is less important to be chosen right. Thedetrended data between the boundaries is just roughlycut away. This is what people also do by visualinspection. As FSV wants to conform with the visualinspection, this rough method gives good results.

Figs. 16 and 17 show the results after denoising thefigures.

It is obvious that choosing a boundary is the weak partof this method. Choosing the boundary is subjective,

where FSV just wants objectivity. A way to help theinterpretation is by processing the detrended data on alinear (mV) instead of a logarithmic scale (dB mV). By

this, the histogram is smaller, with some values forbins far from the histogram, indicating real feature.

Table 4 shows that, after preprocessing, the resultsare more in accordance to the visual perception.

4.3 Multiple measurements

The previous method cancels the entire noise band. Thiscan lead to errors, as specific peaks can be hidden in thatnoise band. These peaks are even by visual inspectionnot noticed, so it is also not the task of FSV to do so.Nevertheless, finding real peaks can be a method tocancel the noise as noise creates random peaks. Amethod to see these real features is by performingtime-consuming quasi-peak measurements. As

mentioned before, the quasi-peak measurement has avery small noise band. However, there is no possibilityto perform peak measurements and conclude what thequasi-peak result would be, because there is a lack ofinformation on the repetitive nature of the signal.However, multiple peak measurements can give anidea of this repeatability and can cancel out part of thenoise. Combining n ND data set of noise decreases thestandard deviation with 1=

ffiffin

p. This can be proved as

follows.

For a set of unrelated samples X, the estimator is

defined as E[X]. The mean value and variance can be

Figure 12 Detrended data (Fig. 8a)

Figure 13 Detrended data (Fig. 9a)Figure 11 Trend of the data (Fig. 9a)

Figure 10 Trend of the data (Fig. 8a)


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written as follows

m E[X] (7)

Var[X] s2 E[(X m)2] (8)

The linearity of the estimator can be proved

E[a X b Y] a E[X] b E[Y] (9)

Equation (8) can be rewritten as

Var[X] E[X2] 2 m E[X] m2 (10)

Using (7)

Var[X] E[X2] (E[X])2 (11)

The variance has the following properties [7]

Var[a X] a2 Var[X] (12)

Var

XiXi

" #Xi

Var[Xi] (13)

Consider n independent data sets Xi. It has to be proved

that

s

Pni1 Xin

s(Xi)ffiffi

np (14)

where s(X) is the standard deviation of the data set X.

From (12), it is clear that

Var

Pni1 Xin

! 1

n

2Var

Xni1

Xi

" #(15)

Figure 16 Preprocessed data (Fig. 8a)

Figure 17 Preprocessed data (Fig. 9a)Figure 15 Histogram (Fig. 13)

Figure 14 Histogram (Fig. 12)


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Using (13) results in

1n

2 Var Xni1

Xi" # 1

n

2 Xni1

Var[Xi]( )

(16)

For n data sets, randomly generated under the sameconditions, the variance is

8Xi, Xj:Var(Xi) Var(Xj) (17)

For n uncorrelated data sets, this can be rewritten as

1

n 2 X

n

i1 Var[Xi]

( ) 1n 2

n Var[Xi]

Var[Xi]n

(18)

Combining (18) and (8) gives (14).

A question that can be asked is how many peakmeasurements need to be combined to decrease thenoise band to the level of the quasi-peak noise band.Consider the quasi-peak measurement of Fig. 5a.After detrending the line, the noiseband has a standard

deviation of 1.25. One peak measurement (Fig. 3a)has a standard deviation of 2.63. To reach a noiseband with a standard deviation of 1.25, according to(14), approximately five peak measurements have tobe combined. The benefit here is the objective natureof the method.

There is another way to deal with these figures. Theratio between the standard deviations for peak andquasi-peak for the noise band is 2.63/1.25 2.10 or6.46 dB. This is very close to the 6.7 dB, mentionedbefore (Section 3.1).

Applying this to real data results in Fig. 18. With thecombination of several peak measurements (five

measurements on Fig. 18), it is obvious that the morethe combinations, the better the quasi-peakmeasurement reached. The advantage in comparisonto the histogram method is that no decisions onborders have to be made. However, fivemeasurements including the calculation are equallytime-consuming as one quasi-peak measurement. Thismethod is not further investigated.

5 Combination of FSV and IELF

5.1 Introduction

Until now, two different problems have been discussed.Noise corrupts the FSV results and the differencebetween the visual interpretation of linear data on alogarithmic axis and the FSV interpretation. The first

problem is solved by the histogram method, whereasthe IELF method accounts for the second problem.The conclusion can be made that the best solution is acombination of these two. There are two possibilities:

preprocessing the data to denoise and evaluate byIELF,

convert the data measured in linear steps to alogarithmic equivalent, preprocess the data to decreasethe noise and evaluate by FSV.

The first method seems to be the easiest. The secondtakes much more time, but has the benefit it can finallybe integrated in the FSV method. In this way, the area ofapplication of FSV is increased.

Figure 18 To p: o ne p ea k m ea su rement ; m id dl e:combination of five peak measurements; bottom: quasi-

peak measurement

Table 4 Rough data in comparison to

preprocessed data with histograms, logarithmic

noise elimination and FSV

GDM GDM after preprocessing

identical 0 0

peak 0.461 0.241

average 0.414 0.224

quasi-peak 0.159 0.183

UD noise 0.658 0.697

ND noise 0.687 1.981


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5.2 Changing linear to logarithmic datasets

Conducted EMC measurements are performed between150 kHz and 30 MHz. Measurement frequenciesincrease by linear steps. When visualising data, a

logarithmic axis is used. By visual inspection, theemphasis is on the lower frequencies, whereasmathematical methods stress higher frequencies. Tohave the same weight visually and mathematically, theamount of data points also has to be spreadlogarithmically.

Consider a set ofn datapoints representing a normalEMC measurement. This data set is equally spaced witha distance Dfi for all points i.

To create the logarithmic data, there are two

possibilities. The number of data points n can be keptconstant or the maximum distance between two datapoints can be kept constant and equal to the currentdistance Df

n. The second possibility is chosen, because

in that case no information is lost. The largest stepDf

n

0 is equal to Dfn. All the other steps are calculated

logarithmically. The new data points are calculated bylinear interpolation. This results visually in twoexactly same data sets, except that the logarithmicdata set contains much more data points (five timesmore in the case of conducted emission).

For the calculation of each step, the relative increase xcan be calculated by

fn fn1(1 x) (19)

The distance between the last points is set to themaximum step

fn fn1 Dfn (20)

Combining these two gives

x fnDfn

(21)

The number of steps n can be calculated by writing fn

asa function of f1

fn f1(1 x)n1 (22)

n log( fn=f1)

log (1 x) 1 (23)These n points have to be calculated, starting with f1,

where

fi fi1(1 x) (24)

Evaluating the discussed typical data sets by FSV

(Figs. 3a, 4a and 5a), linearly and logarithmicallyspaced, gives the results shown in Table 5. These datasets are not yet denoised in order to see only theeffect of making the data set logarithmic.

It can be noticed that for the logarithmic data set, theFSV value is higher than for the linear. There are tworeasons for this. The logarithmic data set containsmore data points. As these data are not denoised,there is more noise. The second reason is that themore the data points, the more the differences can beseen by FSV. This is important to notice. The

conclusion can be made that FSV is valuable for arelative comparison between several data sets, but lessvaluable to give an absolute value to the comparison.Preprocessing data lowers the GDM result as expected(Table 6). For the linear data, a 100-point SMAhas been used or 1.7% of the total length.The logarithmic data contains five times more datapoints. Therefore a 500-point SMA has been tested,giving other results. The relative classification ofbest to worst does not change, but the absolute resultdoes.

6 Example: ranking ofmeasurement methods for EMI

6.1 Introduction

To test the proposed methods, a real application, wherevalidation is needed, is used.

Testing conducted and radiated emission in largesystems is not always possible in an EMC testlaboratory. Besides the large physical dimensions, largeassemblies or systems need special arrangements forpower supply. In some cases, even only in situ

Table 5 FSV results of the linear and

logarithmic data sets

GDM linear GDM log

identical 0 0

peak 0.461 0.607

average 0.414 0.491


UD noise 0.658 0.822

ND noise 0.687 1.350


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assembling of the systems is possible, leading to the needfor in situ measurements.

Performing in situ measurements of conductedemission is not always possible by using an LISN. Thisis especially the case when testing large systems,where an LISN with a high current capacity is needed.As there is a demand for alternative test methods,special methods for emission and immunity for largemachines are defined. Testing conducted emission inthe range of 150 kHz to 30 MHz is normally done byan LISN or a VP, as proposed by European Standards[9, 10]. For large machines, an LISN with a highcurrent capacity is needed and a high power isolationtransformer is favoured. In many cases, this type of

equipment is too expensive, or even not available. Theproposed alternative test methods are the capacitiveVP (CVP), the capacitive foil probe (CFP) and theEFT clamp, normally used to test immunity againstfast transients, and now used as a sensor for emission.These methods were discussed in [1113].

6.2 Alternative test methods

6.2.1 LISN as VP or parallel: As a first alternativemethod, the LISN is connected to the mains (Fig. 19).This is still complying with CISPR 16-1 standard. For

these measurements, inductances of 50 mH are placedin series with the EUT, to filter the mains. In a real insitu measurement, this is not the case. For a correctconnection, notice that the mains input of the LISN isleft unconnected. Fig. 20 shows the resultingmeasurement and the difference between thismeasurement and the reference measurement.

It is seen that there is nearly no difference betweenboth measuring methods. In fact, the only differencein the setup is the lack of Y-capacitors between wiresand PE connection. Especially at higher frequencies,

the effects of the (LC) filter of the LISN interactingwith the EUT and the power network are observed,showing differences of about 10 dB. However, this is a

well-known resonance effect, even observed whenusing another LISN with the same EUT.

6.2.2 Voltage probe: The 50/1500 V VP used is theone as specified in CISPR16-1 and proposed as analternative measuring setup in EN 55011. Themeasuring results are shown in Fig. 21.

The measurement with the VP is executed as statedin the standard EN 55011. It is well known that somedifferences are observed in the beginning of the MHzregion.

6.2.3 Capacitive VP: The main advantage of the CVPin comparison to the other capacitive methods is the

built-in amplifier, decreasing and flattening theattenuation to 20 dB [12].

The measurement results are given in Fig. 22.Although the CVP probe is originally intended tomeasure conducted emission of data andcommunication cables, it is clear that the probe may

Figure 19 LISN as VP

Figure 20 LISN (black), LISN as VP (grey) and difference

(bottom)

Table 6 FSV result after preprocessing with 100 and 500

point SMA

Log and preprocess

100 points

Log and preprocess

500 points

identical 0 0

peak 0.457 0.337

average 0.354 0.309


UD noise 0.779 0.754

ND noise 1.304 1.300


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to the input of the measuring receiver or a preamplifier.Because of the small capacitance and the 50 V inputimpedance of the measuring equipment, a largeattenuation factor occurs. The use of an appropriateamplifier, as it is integrated in the CVP probe, should

provide better results. Another point is the unknownimpedance Z2, being the capacitance (lowerfrequencies) or impedance (higher frequencies)between the foil and an (undefined) ground. The foilshave a length of 30 cm and 1 m. The largest foil hassmaller attenuation, but is more difficult to handle. Inthis paper the measurements of the largest foil areused (Fig. 24).

As a first conclusion about all alternativemeasurement methods, it may be stated that they allbehave very similar, and a more in-depth validation

criterion is needed to rank their performances.As reference, the visual interpretation of the experts

has been taken. As can be seen from Table 7, thepreprocessed data recalculated for logarithmic spacedfrequency points give the best results. The best,second and worst (1, 2 and 5 in the ranking) arevalidated in the same way by FSV. The ranking for theother measurements is not the same as the ranking byvisual inspection, but also by visual inspection, it isdifficult to reach a consensus. Nevertheless,preprocessing the data and recalculating these to get itlogarithmically spaced, improve the FSV results, when

dealing with EMC measurements.

7 Conclusion

It is very difficult to reach a consensus among expertswhen validating measurement results. To avoid thesediscussions, tools such as FSV can be useful. In thispaper, several methods are discussed to improve theFSV results, to be more in accordance to the visualinspection of a panel of experts.

It is very difficult to know when the modifications to

FSV are needed. Using the results of the group ofexperts as a reference is one method, but as has beendiscussed, most experts agree on the best and worst

measurements, but the other measurements are verydifficult to rank. The ranking of these othermeasurements have also the largest standard deviation,showing a lack of unanimity. This is a possible reasonwhy exactly the same results as the reference groupare not reached. It is difficult to say if there is now anerror or not. The number of participants was rathersmall. One different vote can change the ranking.

For the modified FSV, the data are denoised andchanged to a logarithmically spaced data set instead ofa linear one. As has been shown by a real-lifeexample, the results are much better than those withthe classic FSV. It can be concluded that the largestproblems that can corrupt the FSV results arecountered.

8 References

[1] Available at: http://grouper.ieee.org/groups/emc/emc/

ieee_emcs_-_sdcom_mainpage.htm , accessed July 2007

[2] ARCHAMBEAULT B., PRATAPNENI S., ZHANG L., WITTWER D.C., CHEN J.:

A proposed set of specific standard EMC problems to help

engineersevaluate EMC modeling tools. Proc. IEEE Int.

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[3] ANTONINI G., CICCOMANCINI SCOGNA A., ORLANDI A., RITOTA C.,

DUFFY A.: Applications of FSV to EMC and SI data. Proc.

IEEE Int. Symp. EMC, Chicago, USA, August 2005, vol. 2,

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[4] DUFFY A., MARTIN A., ANTONINI G., ORLANDI A., RITOTA C.: The

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[5] SIMPSON R.J., JONES C.R., MACDIARMID I., DUFFY A., COLEBY D.:

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[6] KNOCKAERT J., CATRYSSE J., BELMANS R.: Comparison and

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[7] STENUMGAARD P.: On digital radio receiver performance

in electromagnetic disturbance environments. Radio

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[8] ORLANDI A., ANTONINI G., RITOTA C., DUFFY A.P.: Enhancing

feature selective validation (FSV) interpretation of EMC/SIresults with grade-spread. Proc. IEEE Int. Symp. EMC,

Portland, USA, August 2006, vol. 2, pp. 362 367

Table 7 Results

Visual Classic

GDM

Preprocessed

linear GDM

Preprocessed

log GDM

LISNVP 2 3 4 2 (0.453)

VP 4 5 5 3 (0.465)

CVP 1 4 1 1 (0.423)

EFT 5 2 3 5 (0.575)

CFP 3 1 2 4 (0.511)


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[9] EN55016-1-1: Specification for radio disturbance and

immunity measuring apparatus and methods. Radio

disturbance and immunity measuring apparatus, 2004

[10] EN50370-1 Electromagnetic compatibility (EMC)

Product family standard for machine tools part 1:

emission, 2005

[11] KNOCKAERT J., CATRYSSE J., BELMANS R.: Comparison of

alternative conducted emission measurement methods by

using FSV and IELF algorithms. Int. Symp. EMC,

Barcelona, Spain, September 2006, pp. 718722

[12] HARLACHER B., STEWART R.: CISPR 22 conducted common

mode voltage measurements along a wire using a

capacitive voltage probe. Proc. IEEE Int. Symp. EMC,

Minneapolis, USA, August 2002, vol. 1, pp. 15

[13] CATRYSSE J.: Workshop TEMCA2, Int. Symp. EMC,

Barcelona, Spain, September 2006

& The Institution of Engineering and Technology 2008 IET Sci. Meas. Technol., 2008, Vol. 2, No. 4, pp. 244257/doi: 10 1049/iet-smt:20070063

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