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Department of Electrical Engineering

Communication Systems: EEEN4341

Submitted to:

Dr. Sadiq Alhuwaidi

Spring 2019

Course Design Project Report

Project Title:

Distance to Fault Measurements of Coaxial Cable

Abdulaziz Alsaleh

ID#: 201700157

Submission Date: 4 april 2019

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ABSTRACTIn telecommunication systems, it is very difficult to find a fault in the long lengths coaxial cables if it is

damaged at certain point. The common techniques used to address the issue is the Time Domain

Reflectometer, which solution is suitable for working at lower frequency bands. The frequency domain

analysis of fault detection and localization in coaxial cables are gaining popularity because of its

accuracy and high frequency band of operation. Frequency domain analysis method involve the use of a

swept signal within the operating frequency band of the coaxial cable. Vector network analyzer is used

to launch a frequency sweep signal into the coaxial cable and a detector situated at the launch end picks

up both the transmitted swept signal and the signals reflected back from any faults along the way. The

magnitudes of reflected signals relate to impedance discontinuities and its location. The purpose of this

work is to use an iFFT, inverse fast Fourier transform, to determine the cable fault and its location. The

scattering parameters, S-parameters, of the good and bad cables are characterized. The analysis of s-

parameters and time domain analysis helped us in finding the fault and its location over an extended

coaxial cable.

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TABLE OF CONTENTS

ABSTRACT........................................................................................................................................................... 2

TABLE OF CONTENTS....................................................................................................................................... 3

LIST OF FIGURES................................................................................................................................................ 4

INTRODUCTION................................................................................................................................................. 5

REDUCE MAINTENANCE TIME AND EXPENSE..........................................................................................6

EXPERIMENT...................................................................................................................................................... 7

EXPERIMENT FAULT DETECTION MEASUREMENT PROCEDURE.......................................................8

DISCUSSION QUESTIONS................................................................................................................................. 9

REFERENCES..................................................................................................................................................... 14

APPENDIX: MATLAB CODE OR ANY EQUIVALENT SOFTWARE SIMULATION TOOL USED......14

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LIST OF FIGURESFigure 1: Flow chart of Matlab implementation..........................................................................................7

Figure 2: Flow chart of Matlab implementation..........................................................................................9

Figure 3: Flow chart of Matlab implementation........................................................................................10







Figure 10: Flow chart of Matlab implementation......................................................................................14

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Introduction

Distance to Fault (DTF) is a measuring technique to identify a fault in the transmission line including

coaxial cable. The technique applied generally are time domain reflectormeter (TDR) and frequency

domain reflectometry (FDR) measurement technique. FDR is used to identify the fault identification and

isolation technique used in the transmission line including coaxial cable and other transmission media.

This technique uses a swept RF signal instead of pulses and is far more sensitive than TDR in fault

identification as well as the localization.

Most of the transmission lines and coaxial cables are not equipped with DTF capability that severely

affect the repair of such transmission lines and make it impossible to do the preventative maintenance

procedures. Radio frequency (RF) failure conditions are not measurable using tool such as TDR and

spectrum analyzers with tracking generators. It is impossible by using such devices to detect small

performance changes at higher frequencies and hence it impossible to monitor performance degradation

between maintenance intervals with such techniques [1].

Numerous segments of communication system may suffer from different faults. Transmission lines are

the mostly faced faults at various points. Tower mounted transmission lines may suffer due to climate,

lightning can disjoin the transmission line segments. Poorly tightened connectors and poor

environmental seals may be exposed to acidic rain corrosion. This lead to failure of transmission lines.

These common problems can cause unwanted signal reflections. Using the techniques of DTF available,

the root causes of RF problems can be identified earlier and it also help in identifying the fault location

[2].

In a Wireless Communication System, the cables are replaced perhaps every five to ten years, in some

cases. They are doing so based on the assumption that maintenance calls are imminent on other feeds in

addition to the problem cable. The replacing of all such cables frequently is an expensive process while

on the other hand, it is quite economical to monitor such cables and transmission lines for any fault or

slight degradation and fix the problem on right time [2].

The exponential rise of the communication devices and system, there is an increasing trend of the use of

the cables in the modern communication systems, vehicle communication system and power distribution

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systems. To analyze such systems for any fault, there is a need to cable analysis in case of failure to

precisely identify the faults [3]. Various techniques have been developed for the detection and location

of cable fault [4–5]. The approach used is to send a high-frequency signal and record the the reflected

signal includes information about changes of cable impedance and can be therefore used to detect open

and short circuits.

Experiment1 Consider having two coaxial cables classified as a good cable and a bad cable being used in a

communication network in the frequency range of 10 MHz to 18 GHz. At such high frequencies,

measuring voltages and currents is a challenging task because it involves the magnitude and phase of a

wave traveling in a specific direction. In general, when dealing with high frequency networks,

equivalent voltages and currents, and their related impedance and admittance can be very abstract. To

make direct measurements of electrical systems at high frequencies, the ideas of incident, reflected, and

transmitted waves are used which are given by scattering matrix. Such concept can be interpreted in

terms of a light wave traveling along transmission line as shown in Figure 1. The light travels through an

optical lossy lens which results in reflection and transmission. At high frequencies, the optical lens is

replaced with a material under test, MUT.

2

3 Figure 1: Electromagnetic concept of measurement at high frequency.

4 The purpose of this experiment is to use an iFFT, inverse fast Fourier transform, to determine where

a cable fault has occurred. The scattering parameters, S-parameters, of the good and bad cables are

measured using a vector network analyzer, VNA (attached). The physical lengths of the good and

bad cables are 70 inches (1.778 m) and 120 inches (3.048 m), respectively. When performing the

measurements, the number of points used for the VNA are calculated based on the following

equations:

6

5 Dr=c2

.v p

f max− f min−−−−−−−(1)

6 DT=N . Dr−−−−−−−−−−−(2)

7 Where

8 N=Number of sample pointsDr=Distance resolutionDT=Totaldistance rangevp=velocity factor

The vp is equal to the reciprocal of the square root of the dielectric constant (relative permittivity) of

the material through which the signal passes. The total distance range is the physical length of the

cable. For the given coaxial cable, vp is 0.69.

Experiment Fault Detection Measurement ProcedureThe flow chart of the Matlab code implementation of the experiment as discussed in previous section is

given in Figure 2.

Figure 2: Flow chart of Matlab implementation

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The Matlab code mainly consisted of the analysis of the s-parameters along with time domain analysis

of the transmitted and reflected signal. The frequency domain analysis measurement technique requires

a swept frequency input to the transmission line. An inverse FFT (iFFT) is performed on the reflected

signals transforming this information into the time domain. The distance is then calculated from this

information by knowing the propagation velocity. The relative propagation velocity of a coaxial

transmission line is required for distance calculation. The results and analysis of the both type of cables

are provided in results and discussion section.

Discussion Questions Task-1: Analyze the measurements by reasoning why such number of points were used for each cable.

The frequency domain measurement technique for distance to fault detection (DTF) requires a swept

frequency input to the coaxial. An inverse iFFT is performed on the reflected signals transforming this

information into the time domain. The distance is then calculated from this information by knowing the

propagation velocity. The relative propagation velocity of a coax transmission line is required for

distance calculation. When performing the measurements, the number of points used for the VNA are

calculated based on the following equations:

Dr=c2

.v p

f max−f min−−−−−−−(1)

DT=N . Dr−−−−−−−−−−−(2)

Where

N=Number of sample pointsDr=Distance resolutionDT=Totaldistance rangevp=velocity factor range

The vp is equal to the reciprocal of the square root of the dielectric constant (relative permittivity) of the

material through which the signal passes. The total distance range is the physical length of the cable. For

the given coaxial cable, vpis 0.69.

The scattering parameters (S11 and S12) of both bad cable and good cable are shown in Figure 3 and

Figure 4. From Figure 3, it is clear there is no reflection from the good cable. The values are lower than -

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18 dB over the entire band while it is not true for reflection coefficient of bad cable. Figure 4 shows that

there is transmission of power from input to output of good cable with certain attenuation. It is not true

for bad cable. Most of the power is lost before reaching to end terminal.

0 5 10 15-40

-30

-20

-10

0

Frequency (GHz)

S-p

aram

eter

s [d

B]

S11 of good cable

S11 of bad cable

Figure 3: S11 of good cable and bad cable

0 5 10 15-40

-30

-20

-10

0

Frequency (GHz)

S-p

aram

eter

s [d

B]

S12 of good cable

S12 of bad cable

Figure 4: S12 of good cable and bad cable

For DTF, it is important to understand the difference between fault resolution and distance resolution

since the meanings are different. The fault resolution is the systems’ ability to separate two closely

spaced signals. For example, two discontinuities located 0.5 ft apart from each other will not be

identified in a DTF measurement if the fault resolution is 2 ft. Because DTF is swept in the frequency

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domain, the frequency range affects the fault resolution. A wider frequency range therefore means better

fault resolution and a shorter maximum distance. Similarly, a narrower frequency range leads to wider

fault resolution and greater maximum horizontal distance. The only way to improve the fault resolution

is to increase the frequency range.

For the given bad cable data, we have plotted the return loss vs time. It is evident from the given figure

that the fault is obvious as maximum energy reflected from the fault location. The number of points

selected were enough to cover the fault location and distance resolution is lower than the fault

resolution.

Task-2: Plot S-parameters magnitude data in dB versus frequency.

The S-parameters of both good cable and bad cable are plotted in Figure 5 and Figure 6, respectively.

0 5 10 15-40

-30

-20

-10

0

Frequency (GHz)

S-p

aram

eter

s [d

B]

S11S22S12S21

Figure 5: S-parameters of good cable

10

0 5 10 15-40

-30

-20

-10

0

Frequency (GHz)

S-p

aram

eter

s [d

B]

S11S22S12S21

Figure 6: S-parameters of bad cable

Task-3: Plot time and distance domains data.

The given S-parameter data is imported into matlab. The given data was transformed to

time domain using iFFT command in mtalab. The reflected signals vs time and distance

are shown in Figure 7 and Figure 8 for good cable while Figure 9 and Figure 10 shows

the reflected signals vs time and distance for bad cable data.

0 2 4 6 8 100

5

10

15

20

Time (ns)

Ref

lect

ion

Coe

ffici

ent s

11 [d

B]

Cable to connector

P-2 Connector

P-1 Connector

11

Reflected signal [dB]

Figure 7: Reflected signal vs time for good cable

0 0.5 1 1.5 20

5

10

15

20

Distance(m)

Ref

lect

ion

Coe

ffici

ent s

11 [d

B] P-1 Connector

P-2 Connector

Cable to connector

Figure 8: Reflected signal vs distance for good cable

0 5 10 150

1

2

3

4

5

6

Time (ns)

Ref

lect

ion

Coe

ffici

ent s

11 [d

B]

P-2 Connector

Cable FaultP-1 Connector

Figure 9: Reflected signal vs time for bad cable

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0 1 2 3 40

1

2

3

4

5

6

Distance(m)

Ref

lect

ion

Coe

ffici

ent s

11 [d

B]

P-2 Connector

Cable FaultP-1 Connector

Figure 10: Reflected signal vs distance for bad cable

Task-4: Label what all the peaks on your time and distance plots correlate to in terms of connectors, cables.

All the plots in Figures 7 to 10 are labeled for faults occurrence, connector connection and loose connection between

cable and connector. P-1 connector is assumed to be on the input side while P-2 is port connection to output. The

reflection of the signals are mainly from the cable and connector connection and connectors connecting the port-1 and

port-2 of VNA. All the faults are labeled from where it is coming.

Task-5: Compare the physical measurement of the good and bad cables to the obtained plots in the distance domain

The plots in distance domain are accurately corresponding to the physical length of both the cables, bad and good

cables.

The matlab code is provided in the Appendix that shows how we have converted the time domain signal to distance

domain signal by taking into account the velocity factor of 0.69 for coaxial cable. The following observations are made

for both the cables.

For good cable: The reflected signals are mainly form the end connectors. There is no reflection from the middle or

any part of the conductor. Also, from the transmission coefficient S12, it is clear that most part the signal is transmitted

and there is only attenuation loss throughout the cable.

For bad cable: The distance domain reflected signals as depicted from the given Figure 10, are mainly form the

connector at the input port P-1 and the cable. The transmission coefficient S12 curves clearly depicts that the signal

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could not transmit through the cable. The signal is reflected at distance of a 1.668 m from the P-1. Hence, this

observation is really helpful in determining the fault and its location.

Conclusions

In this work, we have used an iFFT, inverse fast Fourier transform, to transform the frequency domain signal to time

domain signal to determine the cable fault and its location. Distance to fault is a key measurement method that is used

to analyze the fault in the coaxial cable and location of its occurrence. The magnitudes of reflected signals relate to

impedance discontinuities and its location. The scattering parameters, S-parameters, of the good and bad cables are

characterized. The analysis of s-parameters and time domain helped us in finding the fault and its location over an

extended length of coaxial cable. All the simulation and finding are discussed in the results and discussion session of

the report.

References[1] Shi, Q., Troeltzsch, U., & Kanoun, O., “Detection and localization of cable faults by time and frequency domain measurements”, In 7th IEEE International Multi-Conference on Systems Signals and Devices (SSD), 2010 (pp. 1-6).

[2] Time Domain Reflectometry Theory, Application Note 1304-2, Agilent Technologies, Aug. 2002, www.agilent.com.

[3] Time Domain Reflectometry Theory, Application Note 1304-2, Agilent Technologies, Aug. 2002, www.agilent.com.

[4] Shin YJ, Powers EJ, Choe TS, Hong CY, Song ES, Yook JG, Park JB. “Application of time-frequency domain reflectometry for detection and localization of a fault on a coaxial cable”, IEEE Transactions on Instrumentation and Measurement. 2005 Dec;54(6):2493-500.

[5] hung, Y. C.; Furse, C. & Pruitt, J. “Application of phase detection frequency domain reflectometry for locating faults in an F-18 light control harness Electromagnetic Compatibility”, IEEE Transactions on, 2005, 47, 327-334.

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Appendix: MATLAB Codeclc clear allclose all % ===================================% S parameters of good and bad cables% ===================================s_goodcable=csvread('goodcable_01.csv',0,0);s_badcable=csvread('badcable_01.csv',0,0);% =================================% Distance resolution% =================================f_goodcable=s_goodcable(1:end,1)/10^9;f_badcable=s_badcable(1:end,1)/10^9; figure(1)plot(f_goodcable,s_goodcable(:,2),'b','linewidth',2.5)hold online_fewer_markers(f_goodcable,s_goodcable(:,8),25, 'g','MFC','b','Mks',1,'linewidth',2.5,'spacing','curve','LegendLine','on');line_fewer_markers(f_goodcable,s_goodcable(:,4),40, '*r','MFC','k','Mks',8,'linewidth',2.5,'spacing','curve','LegendLine','on');line_fewer_markers(f_goodcable,s_goodcable(:,6),30, 'dk','MFC','r','Mks',1,'linewidth',2.5,'spacing','curve','LegendLine','on'); legend('S_1_1','S_2_2','S_1_2','S_2_1','Location','southwest')set(gca,'fontsize',17)xlabel('Frequency (GHz)','FontSize',15)ylabel('S-parameters [dB]','FontSize',15)grid onaxis([0 18 -40 0]) figure(2)plot(f_badcable,s_badcable(:,2),'b','linewidth',2.5)hold on% line_fewer_markers(f_badcable,s_badcable(:,8),25, 'g','MFC','b','Mks',1,'linewidth',2.5,'spacing','curve','LegendLine','on');line_fewer_markers(f_badcable,s_badcable(:,4),40, '*r','MFC','k','Mks',8,'linewidth',2.5,'spacing','curve','LegendLine','on');line_fewer_markers(f_badcable,s_badcable(:,6),30, 'dk','MFC','r','Mks',1,'linewidth',2.5,'spacing','curve','LegendLine','on'); legend('S_1_1','S_2_2','S_1_2','S_2_1','Location','southwest')set(gca,'fontsize',17)xlabel('Frequency (GHz)','FontSize',15)ylabel('S-parameters [dB]','FontSize',15)

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grid onaxis([0 18 -40 0]) figure(3)plot(f_goodcable,s_goodcable(:,4),'r','linewidth',2.5)hold on% line_fewer_markers(f_badcable,s_badcable(:,4),25, 'g','MFC','b','Mks',1,'linewidth',2.5,'spacing','curve','LegendLine','on');legend('S_1_2 of good cable','S_1_2 of bad cable','Location','southwest')set(gca,'fontsize',17)xlabel('Frequency (GHz)','FontSize',15)ylabel('S-parameters [dB]','FontSize',15)grid onaxis([0 18 -40 0]) figure(4)plot(f_goodcable,s_goodcable(:,2),'r','linewidth',2.5)hold on% line_fewer_markers(f_badcable,s_badcable(:,2),25, 'g','MFC','b','Mks',1,'linewidth',2.5,'spacing','curve','LegendLine','on');legend('S_1_1 of good cable','S_1_1 of bad cable','Location','southwest')set(gca,'fontsize',17)xlabel('Frequency (GHz)','FontSize',15)ylabel('S-parameters [dB]','FontSize',15)grid onaxis([0 18 -40 0])

% ======================================================% Time domain and distance domain analysis of Bad Cable% =====================================================clc clear allclose all% Mode 1 simulated data% =================================s_badcable=csvread('badcable_01.csv',0,0);% =================================% Distance resolution% =================================f_badcable=s_badcable(1:end,1)/10^9; format longdis_res_bad_cable=(1.5*10^8)*(0.69/(1.799*10^10));N1=ceil(3.048/dis_res_bad_cable); % Actual number to be takenN2=1024; % points taken as power of 2Fs=36*10^9; % sampling frequencydt=1/Fs; % differential time tm=dt*N1; %t=0:tm/(N1-1):tm;t=t*10^9;distance_bad_cable=512*dis_res_bad_cable;d=0.69*3*10^8*10^-9*t;

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figure(1)rho1=s_badcable(:,2);theta1=deg2rad(s_badcable(:,3));x=rho1.*exp(j*theta1);y=ifft((x));plot(t,abs(y(1:N1,1)),'b','LineWidth',2) % legend('badcable s_1_1','badcable s_2_2','Location','southwest')set(gca,'fontsize',17)xlabel('Time (ns)','FontSize',15)ylabel('Reflection Coefficient s_1_1 [dB]','FontSize',15)grid ondim = [.2 .5 .3 .3];str = 'P-1 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'Cable Fault';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'P-2 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) figure(2)rho1=s_badcable(:,2);theta1=deg2rad(s_badcable(:,3));x=rho1.*exp(j*theta1);y=ifft((x));plot(d,abs(y(1:N1,1)),'b','LineWidth',2) % legend('badcable s_1_1','badcable s_2_2','Location','southwest')set(gca,'fontsize',17)xlabel('Distance(m)','FontSize',15)ylabel('Reflection Coefficient s_1_1 [dB]','FontSize',15)grid ondim = [.2 .5 .3 .3];str = 'P-1 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'Cable Fault';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'P-2 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15)

% ======================================================% Time domain and distance domain analysis of good Cable% =====================================================clc clear all

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close all% =================================s_goodcable=csvread('goodcable_01.csv',0,0);% =================================% Distance resolution% =================================f_goodcable=s_goodcable(1:end,1)/10^9; format longdis_res_good_cable=(1.5*10^8)*(0.69/(1.799*10^10));N1=ceil(1.778/dis_res_good_cable); % Actual number to be takenN2=512; % points taken as power of 2Fs=36*10^9; % sampling frequencydt=1/Fs; % differential time tm=dt*N1; %t=0:tm/(N1-1):tm;t=t*10^9; distance_good_cable=N1*dis_res_good_cable; d=0.69*3*10^8*10^-9*t; figure(1)rho1=s_goodcable(:,2);theta1=deg2rad(s_goodcable(:,3));x=rho1.*exp(j*theta1);y=ifft((x));plot(t,abs(y(1:N1,1)),'b','LineWidth',2) % legend('badcable s_1_1','badcable s_2_2','Location','southwest')set(gca,'fontsize',17)xlabel('Time (ns)','FontSize',15)ylabel('Reflection Coefficient s_1_1 [dB]','FontSize',15)grid on% x = [0.3 0.5];% y = [0.6 0.5];% annotation('textbox',x,y,'String','y = x','FontSize',25)dim = [.2 .5 .3 .3];str = 'P-1 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'P-2 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'Cable to connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) figure(2)rho1=s_goodcable(:,2);theta1=deg2rad(s_goodcable(:,3));x=rho1.*exp(j*theta1);y=ifft((x));

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plot(d,abs(y(1:N1,1)),'b','LineWidth',2) % legend('badcable s_1_1','badcable s_2_2','Location','southwest')set(gca,'fontsize',17)xlabel('Distance(m)','FontSize',15)ylabel('Reflection Coefficient s_1_1 [dB]','FontSize',15)grid ondim = [.2 .5 .3 .3];str = 'P-1 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'P-2 Connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15) dim = [.2 .5 .3 .3];str = 'Cable to connector';annotation('textbox',dim,'String',str,'FitBoxToText','on','FontSize',15)% axis([0.5 5 -25 0])

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