High Quality Circuit Breakers Thermal Inspectionsresearchpub.org/journal/jac/number/vol2-no2/vol2-no2-3.pdf · Section three (right-side): indicates the main report (results), thermal-digital

INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS VOL.2 NO.2 July 2013

ISSN 2165-8277 (Print) ISSN 2165-8285 (Online) http://www.researchpub.org/journal/jac/jac.html

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Abstract-This paper proposes an efficient and economic

method to predict and solve abnormal conditions in all

types of electrical circuit breakers using MATLAB

Graphic User Interface “GUI” with thermal imaging

infrared “IR” camera. In spite of the benefits of thermal

inspections, its actual utilization with traditional

procedures necessitates relying on external inspection

experts. This increases the cost of inspection resulting in

unbeneficial commercial process. The proposed technique

avoids the needing for external inspection experts. Thus,

all electrical equipment can be tested with an economic

manner, where the overall cost can be significantly

reduced. The paper highlights also the benefits of the

proposed technique in providing a complete thermal

profile of the system to define the particular condition or

fault corresponding to the thermal signature. The

performed report defines the problems that found in the

system, suggests remedy and performs any necessary

corrective action in a suitable time and recommends for

the priority of correcting these problems. Accordingly, the

economic aspects of thermal imaging IR cameras will take

new forms, which will enable their actual utilization in

large scales.

I. INTRODUCTION

Faults in electrical equipment and components can

cause many problems that may lead to severe damages in the

equipment and the whole system. Therefore, it is preferable to

predict – by predictive maintenance tools- these faults before

their actual occurrence to prevent the expected damage, which

can be accomplished by thermal imaging, IR, camera [1].

Thermography, or thermal visualization, is a type of infrared

visualization. Thermographic cameras, or shortly thermal

cameras, are used in many heavy factories like metal recycling

factories, wafer production factories, etc., for monitoring the

temperature conditions of the machines and electrical

elements. When there is any malfunctioning of machines (or

other equipment), extra heat will be generated and it can be

picked up by thermal camera. Thermal camera will generate

an image to indicate the condition of machine or equipment.

Thermographic inspection of electrical installations is used in

the three main areas of electric networks: power generation,

power transmission and power distribution. In addition, this

technique can be used in enormous medical and industrial

applications. Besides, infrared cameras can be used as a robust

method for analyzing the various speeds of multitudinous

vehicles by utilizing thermal images as the source of

operation.

Comparing thermal images at normal and abnormal conditions

would simplify many diagnostic techniques. With aids of

thermal imaging, the operator can maintain and monitor all

equipment by just observing their thermal images captured

routinely and displayed on a monitor, even from remote

locations. So, this can improve safety and reduce man power

and maintenance time. Since overheat cannot be visually

recognized in some devices, thermal imaging monitoring has

to be utilized to prevent sudden accidents.

Regardless of the numerous benefits of thermal inspections,

they have some problems in the actual utilization such as

increased investment in staff training and ignoring the

economical benefits of this process by the management. In

addition, there is a significant problem regarding increased

investment in diagnostic equipment including cost of

purchasing inspection hardware and software, upgrading and

calibrating software, setting up a predictive maintenance

database, and developing the final report. These problems

cause expensive and complex inspections, which may

discourage the use of such methods [2-9].

There are many modifications and developments in the type

of predictive maintenance such as a computerized aintenance

management system “CMMS” that is introduced in [10]. The

implementation of this method as a new technology in

High Quality Circuit Breakers Thermal Inspections

I. Bedir Ahmed M. Azmy F. Selim

Elec. Power and Machines Engineering

Department, Faculty of Engineering,

Tanta University

Head of Elec. Power and Machines

Engineering Department, Faculty of

Engineering, Tanta University

Elec. Power and Machines Engineering

Department, Faculty of Engineering, Kafr

Elshiekh University, Egypt

[email protected] [email protected] [email protected]



02

maintenance programs is very expensive, where its cost can

reach $100,000, which represents unacceptable cost for most

projects. The same reference introduced infrared camera, IR,

applications with total costs exceed $19,500 without software

report method. Another technique for updating sequential

predictive maintenance "USPM" policy is used in [11]. It

provided a cost-effective manner to decide a real-time

predictive maintenance schedule for continuously monitored

degrading systems that minimizes maintenance cost rate

"MCR " in the long term. However, this method did not

consider the unavailability of all information about

investigated equipment (maintenance history). Furthermore,

complex calculations are required to analyze the obtained data,

and no software is developed to facilitate the real

implementation to operators. Besides the two previous

techniques, there is another system known as “infrared

thermography anomaly detection algorithm” (ITADA) that is

proposed in [12]. In this system, a computerized system is

developed depending on a combination of artificial

intelligence and digital image processing techniques to give

automatic decisions. According to [13] and [14], the main

decision to define abnormal conditions is based on

computational intelligence techniques and digital image

processing. The diagnosis tool uses a set of neuro-fuzzy

networks to generate a thermovision diagnosis by identifying

variables related to inspected element and other variables

affecting decision accuracy. All the previous techniques have

some problems with management observations such as: the

high initial costs of tools, dependency mainly on the history of

equipment maintenance schedule, needing another digital

image processing techniques and so on.

The new proposed technique is used to fulfil the requirements

of thermal imaging inspections with minimal costs. The

benefits of the inspection can cause net saving in some cases.

Through the proposed technique, which uses a Matlab GUI

program aided by thermal camera, a deep investigation of the

economic aspects related to the thermal inspection will be

introduced. To emphasise that the problems associated with

this trend can be overcome, a proposed technique will be

presented to facilitate the inspection and analysis of the

obtained results. The proposed technique provides a

management tool for the operator that eliminates the need for

highly-experienced technician. The objective is to introduce a

general framework for the inspection process that can

significantly reduce the inspection cost. The analysis ensures

the possibility of providing an economic manner to

accomplish the inspection with an easy and accurate manner.

II. APPLICATION OF CONVENTIONAL SOFTWARE

Historically, infrared cameras were relatively expensive

and had short service life. In addition, they required high

maintenance and considerable skill and training for operators.

The conventional utilization of these cameras depends on

contracting with other professional inspection company to

accomplish inspections with accompanied high costs.

III. PROPOSED METHODOLOGY

In this study, a proposed Matlab program, interfaced with

Graphical User Interface" GUI", is developed containing all

information about various circuit breakers "CBs" from famous

manufacturers. In fact, it is intended to simplify the program

to any technical operator who can use the IR camera to capture

and save thermal image(s) and has only basic information

about the inspected CBs. A proposed enhanced technique is

used to fulfil the requirements of thermal imaging inspections

with minimal costs. The benefits of the inspection can cause

net saving in some cases [15, 16].

A. Program Methodology

The main procedures of the inspection program of

CBs is simplified as shown in Figure (1). The program studies

different CBs abnormality in detail as will be shown later

through examples.

In Figure (2), a MATLAB GUI shows the modules of

programs that are used to diagnose all CBs categories. In this

figure, the GUI report is introduces, which is divided into

three sections:

1. Section one (upper part): provides the possibility of

defining all types of CBs (categories of low, high

voltages, Miniature, Moulded case, .etc.).

2. Section two (left side): where the inputs and outputs are

defined and also it offers guidelines to program user.

3. Section three (right-side): indicates the main report

(results), thermal-digital photos, problem, remedy,

priority, new CB rating if needed, percentage loading and

links to other cable programs.

As shown in Figure (2) the CB basic information can be

defined by a simple and easy manner and thus, the complete

information will be automatically defined in the program and

will appear in the second section. Finally, the technical

operator can complete the information by interring additional

data that must be given to the program.



09

Figure (1). Flow chart of CB inspections by using proposed

Matlab program methodology

B. Steps of CB Inspection The proposed Matlab GUI Program used for CBs

inspection basically depends upon the inputs, which are

inserted by thermographer. Before describing the program, it

is proper to define some symbols that are used in the program

to understand its steps as introduced in Table (1).

The GUI program steps are:

1. The program takes the thermal and digital photos of the

cables (healthy and abnormal) from any IR camera

memory.

2. The bad, healthy and ambient temperatures (T,T1,T2)

can be obtained from the thermal photo, then the

temperature difference can be obtained, diff = T1-T2.

3. The thermographer measures the actual current in the

connected cables to inspected CB in amperes (I) by

clamp ammeter.

4. The CB information, CB type (tp), voltage levels

(cc), voltage ratings (V), number of poles (P), rating

of CB (Ir), etc.

5. According to the internal program steps, loading related

to temperature difference, it can be decided the

problem priority according to Table (2).

Figure (2). The interface window of the program

methodology

After defining the abovementioned information (data),

run button is used to start the processing and the final

complete report will appear as will be shown while discussing

different case studies later. The two columns of input and

outputs will disappear when the report is illustrated (left

corner).

IV. CASE STUDIES

The actual circuit breaker problems that can be analyzed

using thermographic inspection are discussed through the

follows case studies:

Input the system data

( Ia1, Ia2, Ir,T1, T2, T, b, cc, kV, P, DC, kA)

report

Problem: heating in /out of CB without

problem in it, with the same problem in cables

Remedy: please check connections and cable

program

Priority: as per table

report

Problem: 1- heating all internal connections of CB

2- heating in one/two of three.

Remedy: 1- change CB with higher one (Ir new)

2- redistribute all loads equally.

Priority : as per table

End

no no

yes

yes

report

Problem: heating in all internal connections

Remedy: change CB with higher quality one

Priority : as per table



00

Case (1): Heating through CBs output connections, Su = 1

according to table 1.

GUI program results:

In this case, there is a temperature rise in three out connections

of CB, with approximately 49.7 oC (119.3-70) related to

normal points. The actual current of cable “ 1aI ”, is 180

amperes (maximum), and the CB rating is 250 A at normal

temperature. The dependency upon the experiences of

technical operator may lead to misleading decision to the

complete diagnose of the problem (e.g. the decided remedy

may be “change the CB with new one”, which is not correct.

The operator can select the CB different parameters (T, T1, T2,

rI , 1aI , 2aI , cuI , .etc.), and pushes the running button

(depending on the databases in the program) to export final the

report as shown in Figure (3).

The final observations of the inspection report related to this

case study are:

1. There is no problem inside CB connections, while input

cables terminals are heated, Su = 1.

2. The main problem is in cable terminals with CBs

connections, mainly tighten bolts and the loading, LD , of

CB is 72 %.

3. Thus, the problem is “heating on tighten bolts of cable”,

and the remedy is "clean this cable ends and retighten

them good” without going to cable program.

4. The result of the program indicates that the proper CB

for this load is not greater than 200 A, and according to

ambient temperature, T = 34.3 aided by CB temperature

curve, this new CB can carry up to 215 A continuously.

5. The type of new CB is MCCB, 3-poles, 400 V, but the

rating is 200 A (not 250 A), and its company is

Mitsubishi.

6. The priority is “Now”, with “Red colour”. There is an

expected relation curve between difference “ diff ”

rising in temperatures, and the loadings “ LD ”.

Case (2): Heating through internal CBs terminals, Su= 2.

GUI program results:

In this case: there is overheating in the internal terminal of

CB, with approximately a temperature rise of 13.6 oC (43.6-

30) related to normal outer points.

Table (2): Relationship between loading-temperature difference and priority

Temperature difference

(diff)

Loading (LD %) Priority/colour

diff <= 10o C

LD <= 25 Now /Red

25 <LD <= 50 As quickly as possible/Yellow

50<LD <= 75 As quickly as possible/Yellow

75<LD <= 100 As per schedule/Green

LD > 100 Now /Red

10 <diff <= 20o C

LD <= 25 Now /Red

25 <LD <= 50 Now /Red



LD > 100 Now/Red

diff > 20o C

LD <= 25 Now/Red

25 <LD <= 50 Now/Red

50<LD <= 75 Now/Red

75<LD <= 100 Now/Red

LD > 100 Now/Red



02

Symbol Definitions

Low Voltages Medium, high and extra high voltages

T1 Temperature of tested cable(s) Temperature of tested cable(s)

T2 Temperature of healthy cable(s) Temperature of healthy cable(s)

T Ambient temperature Ambient temperature

diff Difference between T1 and T2 Difference between T1 and T2

Ia1, Ia2 Maximum actual current in bad terminal(s), and

minimum current value in healthy (if found)

respectively.

Maximum actual current in bad terminal(s), and minimum

current value in healthy (if found) respectively.

Su(i) The type of faults (problems) in CBs:

i =1, heating in / out of CB without problem in it,

with the same problem in cables

i =2, two cases :

a- heating in all internal connections of CB, Iac<

Ia1.

b- heating in one/two of three connections of CB,

Iac< Ia1.

i =3, heating in all internal connections CB

(one/two or three), Iac= Ia.

The type of faults (problems) in CBs:

i =1, heating in / out of CB without problem in it, with the

same problem in cables

i =2, two cases :

a- heating in all internal connections of CB, Iac< Ia1.

b- heating in one/two of three connections of CB, Iac< Ia1.

i =3, heating in all internal connections CB(one/two or three),

Iac= Ia.

Pr Priority of action to repair or maintenance (table

4.27)

Priority of action to repair or maintenances (table 4.27)

bs, cs Tables of CBs ratings (according to types of

manufacturer)

Tables of CBs ratings (according to types of manufacturer)

MCB/MCCB =1, for Miniature CBs

=2, for Moulded Case CBs

Iac, Ir Current from tables of CB ratings (calculations) Current from tables of CB ratings (calculations)

P =1 for single pole

=2 for two poles

=3 for three poles

=4 for four poles

=1 for single pole

=2 for two poles

=3 for three poles

=4 for four poles

tp (i) Manufacturer

i= 1, Schneider

i= 2, Merlin grin

i=3, IEC standard

Manufacturer

i= 1, Schneider, SF6,

i= 2, ABB

i=3, ENEGOINREST, SF6, i=4, MEC, Vacuum, i=5, MITS,

Vacuum, i=6, ENEGOINREST, Minimum oil

cc(i) i= 1, Low Voltage Ratings i= 2,3 Medium and High Voltages Ratings respectively

V Voltage ratings from 220-690 V. Voltage ratings V

DC =0, for a.c current

=1, for d.c current

=0, for a.c current

=1, for d.c current

Table (1): Symbols and definitions used in A Matlab GUI program



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Figure (3), Export report of CB: Case study no.1

Figure (4) Export report of CB: case studyNo.2



02

The actual current of cable, 1aI , equals 36 amperes, and the

CB rating, rI , is 32 A at normal temperature. Following the

same procedures explained in the previous case study, the

final report is exported as shown in Figure (4).

The final observations from the inspection report are:

1. The main problem is heating inside CB connection

terminal (single pole), Su = 2.

2. Then, the problem is "heating in all internal CB

terminals"and the remedy is "change it with higher

rating one".

3. The result of the program gives; the new CB for this

loads as200 A, and according to ambient temperature

effect, T=31C, this new CB can carry up to 181.3 A

continuously.

4. The type of new CB, Merlin Gering, is DPN type B and

part no. 19258, 1-pole, with rating 40 A.

5. The priority is "Now", with "Red colour".

V. ECONOMIC ANALYSIS OF CONVENTIONAL AND PROPOSED

THERMAL INSPECTION

The conventional analysis, termed “external inspections”,

depends on delegating outside contractors to accomplish the

electrical inspections of equipment. But, the proposed thermal

inspection, termed "internal inspection", depends on the

technical or engineer in the same inspected site. In the

following, an example for actual implementation steps of the

proposed technique in an Egyptian location(Smart Village, in

Cairo-Alexandria fast desert road) is presented.

1. Collecting data about all inspected equipment in the site

The first step is to collect enough data about all equipment

and categorize them in groups according to their type,

location…etc. The capital cost of the inspected equipment

equals about 18603000 ₤ (≈ 3100500 $).

2. Evaluating the External (Conventional) Inspection

Costs

The second step is to evaluate the annual inspection

cost, which depends on the number, type and status of

equipment. The inspections in this Egyptian site have been

done in the summer season 2008, where the actual total costs

of inspections “V” was 30,000 ₤ (≈ 5000 $). According to the

nature and status of the equipment, at least two inspections

must be done every year (summer and winter), which results

in a total cost of 60,000 ₤ (≈ 10000$). This cost represents

about 0.3225% of the capital cost and hence it forms no matter

in the overall investment. However, it will appear as a large

value when compared to the running cost.

3. Introducing the effect of depreciations on

inspection costs

The depreciation of equipment increases the required

inspections and thus, increases the inspection cost. Assuming

that the value of equipment at starting installation is “P”, the

equipment life time is “N”, the salvage value is “S” and the

depreciation each year is “A”, the straight line method

presented in [17] can be used as follows:

A = N

SP (1)

Applying this criterion on the Egyptian site, the value of

“A” can be decided by supposing the followings:

1. P = 100% (capital cost)

2. N = 20 years (life of equipment "assume")

3. S = 30% (at the end of 20 years)

Thus, the value of “A” can be obtained from equation (1)

to be A = 3.5%. Assuming that, the annual percentage increase

of the inspection cost is the same as the annual percentage of

depreciation of the equipment “A”, thus, the increase of

inspection cost is:

B = A*V/100 (2)

Thus, the inspection cost can be calculated every year as:

1. First year inspection cost = V (0.3225% with respect to P)

2. The inspection cost increase in each year (B) = A*V =

0.01128% (with respect to P)

3. Second year inspection cost = V+B*V = 0.3337% (with

respect to P)

At the Nth year, cost = V (1+B)N-1

(3)

After 20 year for example, inspection cost will be:

0.3225 (1+0.01128)19 = 0.39916% (with respect to P)



02

After calculating inspection costs at the end of each year,

inspection costs will be accumulated as shown in Table (3).

Table 3:Relationship Between Depreciation and Accompanied

Accumulations of External Inspections

Years Value of equip. with 5-20%

and without extension life

External inspections cost (accumulation)

with (5-20%) and without (0%) extension

life of equipment

0% 5% … 20% 0% Acc. 5% Acc. … 20% Acc.

0 100 100 …

100 0 0 0 0 … 0 0

1 96.5 96.7 … 97.1 0.323 0.32 0.323 0.32 … 0.323 0.32

2 93 93.3 … 94.2 0.326 0.65 0.326 0.65 … 0.326 0.65

3 89.5 90 … 91.3 0.33 0.98 0.329 0.98 … 0.329 0.98

4 86 86.7 … 88.3 0.333 1.31 0.333 1.31 … 0.332 1.31

5 82.5 83.3 … 85.4 0.337 1.65 0.337 1.65 … 0.335 1.64

6 79 80 … 82.5 0.341 1.99 0.34 1.99 … 0.338 1.99

7 75.5 76.7 … 79.6 0.345 2.33 0.344 2.33 … 0.341 2.32

8 72 73.3 … 76.7 0.349 2.68 0.347 2.68 … 0.344 2.67

9 68.5 70 … 73.8 0.353 3.04 0.351 3.03 … 0.348 3.01

10 65 66.7 … 70.8 0.357 3.39 0.355 3.39 … 0.351 3.37 . . .

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4. Taking the extension in equipment life time into

account

According to [18] and [19], the life of electrical

equipment is significantly reduced with the temperature

increase, which implies an extension in the life of equipment

with the predictive inspections. The extension in equipment

life is assumed to be in the range from 5 to 20% with respect

to its life time without inspection. Thus, the expected average

life time of equipment will be as follows:

1. 21 years for 5% extension, where the corresponding value

of “A” is: 3.333%


of “A” is: 3.1818%


of “A” is: 3.04347%


of “A” is: 2.91666%

Taking these extensions in equipment life into account,

the percentage change in inspections (B) will be modified to

be: 0.01128% for 20 years, 0.010748% for 21 years,

0.01026% for 22 years, 0.009815% for 23 years and

0.009406% for 24 years. From the abovementioned

discussion, the calculations can be repeated as given in Table

(3), which gives the relationship between the depreciation and

accompanied accumulations of external inspections.

5. Analysing the net inspection cost according to saving in

depreciation

As shown in Table (3), there is a rise in the equipment

salvage value “S” with extension of equipment life time. For

example, the equipment values at the end of the 10th

year

equal 65%, 66.66%, 68.18%, 69.57% and 70.83% with

extensions in the life time of 0%, 5%, 10%, 15% and 20%

respectively. In other words, there is an increase in the salvage

value “S” of: 1.66%, 3.18%, 4.57% and 5.83% for extensions

in the life time of 0%, 5%, 10%, 15% and 20% respectively.

On the other hand, the total (accumulations) inspection costs

at the same year (10th

year) equal 3.392%, 3.3839%, 3.3759%,

3.3707% and 3.36477% for extensions in the life time of 0%,

5%, 10%, 15% and 20% respectively.

It can be concluded that the difference between the saving

in depreciations and the accumulation inspection costs varies

depending on the extensions in the life time, where it can be

negative (the owner will pay more money) or positive (the

owner will save money) as indicated by the net difference. The

net difference for 0%, 5%, 10%, 15% and 20% extension in

the equipment life time are respectively: -3.392% (paid), -

1.7239% (paid), -0.1959% (paid), + 1.1993% (saved) and

+2.46523% (saved). Tables 4 and 5 summarize the previous

calculations for different extensions in equipment life at

different years.

Table 4:Differences Between Savings in Depreciations and

External Inspections Values

Extension

equip. life

Years

Differences as a net saving

for extension in equipment

life from 0% to 20%

depreciations

Accumulation costs of

external inspections for

various cases of extension

in the life time (0%-20%)

with respect to P

0-5% 0-10% 0-15% 0-20% 0% 5% … 20%

0 0 0 0 0 0 0 … 0

1 0.16 0.318 0.45 0.58 0.3225 0.3225 … 0.3225

2 0.33 0.637 0.914 1.166 0.648 0.6484 … 0.64803

3 0.5 0.955 1.37 1.749 0.9783 0.9778 … 0.9766

4 0.66 1.273 1.828 2.333 1.3117 1.3107 … 1.3083

5 0.83 1.59 2.285 2.916 1.6488 1.6472 … 1.643

6 1 1.9096 2.742 3.499 1.9897 1.9873 … 1.9809

7 1.16 2.2276 3.199 4.083 2.334 2.3311 … 2.322

8 1.33 2.545 3.656 4.66 2.683 2.678 … 2.6663

9 1.5 2.8636 4.113 5.249 3.0355 3.029 … 3.01394

10 1.66 3.18 4.57 5.83 3.392 3.3839 … 3.36477

… … … … … … … … …



02

Table 5 Net Cost of External Inspection after Subtracting the

Depreciation Values

Extension

equip.

life

Years

0% 5% 10% 15% 20%

0 0 0 0 0 0

1 -0.3225 -0.1625 -0.0045 0.1275 0.2575

2 -0.648 -0.3184 -0.0114 0.26584 0.51797

3 -0.9783 -0.4778 -0.0226 0.39304 0.77238

4 -1.3117 -0.6507 -0.037 0.5191 1.0247

5 -1.6488 -0.8172 -0.056 0.6408 1.273

6 -1.9897 -0.9873 -0.0755 0.7592 1.51806

7 -2.334 -1.1711 -0.0994 0.8743 1.76094

8 -2.683 -1.348 -0.1286 0.9861 1.9937

9 -3.0355 -1.529 -0.1584 1.0944 2.23506

10 -3.392 -1.7239 -0.1959 1.1993 2.46523

… … … … … …

The previous tables indicate that the inspection process

can cause a net saving due to the resultant extension in the

equipment life time. Even without any extension in the life

time, external inspections result in many benefits in real

application without high cost with respect to the total capital

costs of equipment. For instance, the total inspection cost at

the end of the 20th

year is about 7.1838% neglecting the

extension of life time. On the other hand, if the decreasing in

the depreciation value is taken into account, the total cost of

inspections decreases by significant values from paying

money of 7.1838% to saving money of 4.601%. With different

extensions in equipment life time (N), new tables similar to

Tables (3), (4) and (5) can be derived, to show the benefits of

predictive inspections.

VI. ECONOMIC ANALYSIS DEPENDING ON THE PROPOSED

INSPECTION METHOD

The proposed technique "internal inspections" is a cost-

effective method that can provide many benefits over the

external inspection for thermal inspections. In this case, the

inspection and its reports will be developed by a technician or

any engineer in the site. The detailed descriptions of the

proposed analysis policy, as well as its underlying

assumptions are given as follows:

1. Purchasing a suitable IR camera with appropriate

application capabilities (applied for electromechanical

equipment, moderate ranges of temperatures and

length…etc.). The price of an IR camera with these

properties ranges from 8000 to 12000$. Thus, it is

considered in this research that the IR camera can operate

for at least 10 years with its full efficiency and its average

price is about 10000$.

2. According to the estimated equipment life, N = 20 years,

two IR cameras will be required with a total cost of

20,000$.

3. A certain amount has to be appointed to the calibration

process for the IR cameras each year.

4. The inspection-program cost for the proposed analysis

technique, which uses Graphical User Interface "GUI" aided

by a Matlab programs, has to be identified. The expected

price of the program developed by the author, depending of

the author experience, may be 2500$ (for inspection of

electrical equipment).

1. Methodology

By using a GUI Matlab program containing a database

about all data of various electrical equipment in the site, it will

be easy for any engineer or operator to accomplish the full

inspection process as discussed in details in [15], [16].

2. Calculation of Internal Inspections Costs

Based on the methodology introduced in section (a), the

total cost of the proposed inspection technique can be

calculated as follows:

1. The total cost of the IR cameras and the proposed analysis

programs are calculated for the whole life time of

equipment. This cost is about 22,500 $ for 20 years,

2. The annual cost is then derived: Cost of each year =

22500/20= 1125 $/year,

3. Then, the annual internal inspections cost with respect to

capital cost (P) is calculated as follows:

(1125/3100500)*100= 0.036284%. This means that the

internal inspections cost is about 11.25% of the external

inspection cost and a saving of 0.2862% of the capital cost

can be achieved.

4. The effect of calibration cost is introduced and the total

cost for 20 year inspection is obtained as:

a- The cost of the first year is taken without modifications:

V = 0.036284%

b- A certain value is suggested for the calibration cost of

IR camera “X” with a yearly percentage increase. In

this study “X” is taken as 10% of “V”, with 10%

increase of this values each year.

c- Based on steps “a” and “b”, the cost of all years through

the life time can be calculated as follows:

1) First year = V,



02

2) Second years = V+ X = 1.1 V,

3) Third years = V+X +0.1X = V+1.1X =1.11V, and so

on other years;

For the 20th

year, the cost is 1.555V

Similar to the analysis for the external inspection, the previous

calculations are summarized in Tables 6 and7with their

accumulations with respect to depreciations:

From the above discussion aided by Tables 6 and 7, it can be

concluded that the internal inspection can reduce the

inspection cost considerably and it represents a negligible cost

with respect to the total capital cost of equipment. At the end

of the 10th

year, the net cost equals 0.412284% resulting in a

saving of 2.9797% compared to external inspections for 0%

extension life time.

Taking into account the extension in equipment life

time caused by predictive inspections, the proposed

inspections technique achieves a considerable saving as shown

in Table 7. At the end of the 10th

year for example, the paying

is about 0.412284% for 0% extension in life time, while the

saving is about 5.417716% for 20% extension in life time. The

previous calculations can be applied for different values of

equipment life (N), salvage values (S) and extensions of life

time (N) (0%, 5% ...etc.).

5. Breakpoints for utilizing predictive inspections

This section provides a general framework to enable

higher management to decide for utilizing predictive

inspections. This evaluation is introduced based on the

proposed technique and intended to show whether the

predictive inspection can be accomplished in an economic

manner to save money or not. This can be illustrated by taken

15% extension in equipment life time as example as follows:

Table 6 Differences Between Savings in Depreciations and

Total Internal Inspections

extension

equip.

life

Years

Differences as a net saving

for extension in equipment

life between 0% and 20%

depreciations

Accumulation costs

of internal

inspections for

various cases of

extension in the life

time (0%-20%) w.r.t

capital cost (%)

0-5% 0-10% 0-15% 0-20% Cost accumulati

on

0 0 0 0 0 0 0

1 0.16 0.318 0.45 0.58 0.036284 0.03628

2 0.33 0.637 0.914 1.166 0.03991 0.07618

3 0.5 0.955 1.37 1.749 0.04027 0.011645

4 0.66 1.273 1.828 2.333 0.04066 0.15705

5 0.83 1.59 2.285 2.916 0.04110 0.19815 . . .

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

. 10 1.66 3.18 4.57 5.83 0.04404 0.412284

11 1.83 3.499 5.027 6.416 0.04483 0.457114 . . .

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

. 18 3 5.725 8.226 10.499 0.0529 0.800203

19 3.16 6.043 8.683 11.082 0.0546 0.8548

20 3.33 6.3616 9.14 11.66 0.056437 0.91124

Table 7 Net Cost of Internal Inspection after Subtracting the

Depreciation Values

Extended

equip.

life

Years

0% 5% 10% 15% 20%

0 0 0 0 0 0

1 -0.03628 0.12372 0.28172 0.41372 0.54372

2 -0.07618 0.25382 0.56082 0.83782 1.08982

3 -0.01165 0.38355 0.83855 1.25355 1.63255

4 -0.15705 0.50295 1.11595 1.6709 2.1759

5 -0.19815 0.63185 1.39185 2.08685 2.71785

6 -0.23974 0.76026 1.66986 2.50226 3.25926

7 -0.28184 0.87816 1.9457 2.91716 3.80116

8 -0.32454 1.00546 2.22046 3.33146 4.33546

9 -0.36788 1.13212 2.49572 3.74512 4.88112

10 -0.41228 1.24772 2.7677 4.15772 5.41772

… … … … … …

1. At the end of 20 years, the savings due to extension in

operating life from 0% to 15% is 9.14% (relate to capital

cost P) as shown in Table 7.

2. Equating this value of saving, 9.14%, with total internal

inspections costs, which are 28253 $, the inspection cost

will be completely compensated through the saving due to

extension of equipment life. The equipment capital costs

are calculated from the relation: [(28253/9.14)*100],

which equals 309113.78 $.

3. According to the relation related to Table 7, the net

payment (inspection costs-saving due to extension of

equipment life) will vary according to the following

equation:

10028253

14.9= savingNet P

(4)

where: “P” is the capital equipment costs ($).

The net payment may be negative values (paying money)

or positive values (saving money). Applying equation (4) for



01

all saving values ranging from 5 to 20% aided by Table 6, the

equations to define the economic situation are:

For 5% extension:

10028253

33.3 = savingNet P

(5)

For 10% extension:

10028253

3616.6 = savingNet p

(6)

For 15% extension:

10028253

14.9 = savingNet P

(7)

For 20% extension:

10028253

66.11 = savingNet P

(7)

By using equations 4, 5, 6, 7 and 8, it is possible to select

the economic points (break points) in various cases. Table (8)

can be derived from these equations to illustrate economic

points of internal inspection percentage costs. The idea is

illustrated in a chart form in Figure 5. The detailed

calculations and more results are introduced in [20], and [21].

Table 8 Economic Situation of internal Inspection Costs (%)

Related to Capital Cost

extended

equip. life

Capital cost $

5% 10% 15% 20%

100,000 -24.923 -21.8914 -19.113 -16.593

200,000 -10.7965 -7.765 -4.9865 -2.4665

300,000 -6.0876 -3.056 -0.2776 2.2423

400,000 -3.733 -0.7016 2.0767 4.5967

… … … … …

VII. ECONOMIC ANALYSIS OF PROPOSED THERMAL

INSPECTION WITH DAMAGING RATINGS

Ignoring the predictive inspection causes a damage in

equipment. The damaging cost can be taken into account in

the inspection calculations according to the following

assumptions: The total rate of damaged or failure and

downtime of equipment can vary from at least 0.01% to about

5% (related to capital costs P) for various factors depending

on the preventive/predictive maintenance and surrounding

equipment conditions.

Thus, for the same case study in the previous sections, if

this rating is added of each year (assuming that it is equal to

0.05 % of capital cost for rate of damage equals 1% during

equipment life time), there will be new values for

accumulations inspection costs as can be shown in Table 9.

Figure (6) shows the curves for net accumulated internal

inspection costs with the new values as in Table 10, without

equipment life extension, with 5% equipment life extension,

with 10% equipment life extension, with 15% equipment life

extension, and with 20% equipment life extension. The curves

given in figure 6 illustrate the variation of the accumulative

inspection costs, with respect to the capital cost, with the

equipment life time. This is repeated for different assumptions

for extension in the equipment life time.

The results of Tables (9) and Figure (6) illustrate that;

internal inspection can reduce the inspection cost considerably

and it represents a negligible cost with respect to the total

capital cost of equipment. At the end of the 10th

year, the net

cost equals 0.412284% resulting in a saving of 2.9797%

compared to external inspections for 0% extension life time.

extended equip.

life

Capital cost $

5% 10% 15% 20%

100,000 -24.923 -21.8914 -19.113 -16.593

200,000 -10.7965 -7.765 -4.9865 -2.4665

300,000 -6.0876 -3.056 -0.2776 2.2423

400,000 -3.733 -0.7016 2.0767 4.5967

… … … … …

extended equip.

life

Capital cost $

5% 10% 15% 20%

100,000 -24.923 -21.8914 -19.113 -16.593

200,000 -10.7965 -7.765 -4.9865 -2.4665

300,000 -6.0876 -3.056 -0.2776 2.2423

400,000 -3.733 -0.7016 2.0767 4.5967

… … … … …

Fig. 5 Variation of saving due to inspection with the capital cost for different values of extension life time

-30

-25

-20

-15

-10

-5

0

5

10

15

10

000

0

20

000

0

30

000

0

40

000

0

50

000

0

60

000

0

70

000

0

80

000

0

90

000

0

10

000

00

15

000

00

20

000

00

Net

var

iati

on s

avin

g (

rela

ted

to

P)%

Capital costs P

with extension in N=5% with extension in N=10%

with extension in N=15% with extension in N=20%



22

Taking into account the extension in equipment life time

caused by predictive inspections, the proposed inspections

technique achieves a considerable saving as shown in Table 9.

At the end of the 10th

year for example, the paying is about

0.412284% for 0% extension in life time without taken into

account the rate of damage.

Table (9) Differences Between Savings in Depreciations and

Total Internal Inspections after subtracting the rate of damage

from each year inspection cost

Extension

equip.

life

Years

Differences as a saving

between 0% to 20%

extension in equipment

life(depreciations)

Internal inspection cost

and its accumulations

w.r.t capital cost (%)

after subtracting rate of

damage

0-5% 0-10% 0-15% 0-20% Cost accumulations

0 0 0 0 0 0 0

1 0.16 0.318 0.45 0.58 +0.013716 +0.013716

2 0.33 0.637 0.914 1.166 +0.01009 +0.0238

3 0.5 0.955 1.37 1.749 +0.00973 +0.0335

4 0.66 1.273 1.828 2.333 +0.00934 +0.04286

5 0.83 1.59 2.285 2.916 +0.0089 +0.05177

6 1 1.9096 2.742 3.499 +0.00841 +0.06018

7 1.16 2.2276 3.199 4.083 +0.0079 +0.06808

8 1.33 2.545 3.656 4.66 +0.0073 +0.07538

9 1.5 2.8636 4.113 5.249 +0.00666 +0.082046

10 1.66 3.18 4.57 5.83 +0.00596 +0.088006

11 1.83 3.499 5.027 6.416 +0.00517 +0.09317

12 2 3.817 5.484 6.999 +0.00432 +0.09749

13 2.16 4.135 5.941 7.583 +0.00338 +0.10087

14 2.33 4.453 6.398 8.166 +0.002339 +0.103215

15 2.5 4.77 6.855 8.7495 +0.00121 +0.104425

16 2.66 5.09 7.312 9.33 -0.00004 +0.10438

17 2.83 5.4 7.769 9.916 -0.0014 +0.10298

18 3 5.725 8.226 10.499 -0.0029 +0.100085

19 3.16 6.043 8.683 11.082 -0.0046 +0.09548

20 3.33 6.3616 9.14 11.66 -0.0064 +0.089085

If the rate of damage is taken into account, at the end of

the 10th

year, there will be no payment for inspections but also

there will be saving to management by 0.088006% for 0%

extension in life time. The savings, at the end of 20 years, are

increased with different life extensions as follows: for 5% is

about 3.419%, for 10% is about 6.4506%, for 15% is

about 9.22908%, and for 20% is about 11.749%.

VIII. CONCLUSIONS AND FUTURE WORK

This paper investigates the use of IR cameras and

facilitates its utilization to give optimal solutions of problems

associated with circuit breakers. The results obtained with the

proposed MATLAB GUI program give the following

advantages rather than the traditional techniques that depend

on the experience and manual handling:

1. The flexibility of the technique that enables the diagnosis

of any electrical equipment. In this case, it is required

only to build the databases including all information

about the electrical equipment, which can be

accomplished in an easy manner.

2. With simple modifications in the logic structure and

database, the program can be applied in different

fields including medical, mechanics, civil etc. to

inspect their problems.

3. The obtained results and analysis ensured the possibility

of carrying out the inspection process in an economic

manner.

4. The inspection cost depending on external inspection is

low compared to the capital cost of equipment.



29

Figure (6) Net saving (return due to extended life time –internal accumulated inspection costs after subtracted rate of equipment

damage)

Years/extended equip.

life

0% 5% 10% 15% 20%

0 0 0 0 0 0

1 +0.013716 +0.1737 +0.331716 +0.46371 +0.5937

2 +0.0238 +0.3538 +0.6608 +0.9378 +1.1898

3 +0.0335 +0.5335 +0.9885 +1.4035 +1.7825

4 +0.04286 +0.70287 +1.31587 +1.87087 +2.3758

5 +0.05177 +0.88177 +1.64177 +2.33677 +2.93117

6 +0.06018 +1.06018 +1.96978 +2.8021 +3.55918

7 +0.06808 +1.22808 +2.29568 +3.26708 +4.15108

8 +0.07538 +1.40538 +2.62038 +3.73138 +4.73538

9 +0.082046 +1.588005 +2.94564 +4.195 +5.331

10 +0.088006 +1.75317 +3.268 +4.658 +5.921

11 +0.09317 +1.92749 +3.59217 +5.12017 +6.50917

12 +0.09749 +2.100876 +3.91449 +5.5815 +7.09649

13 +0.10087 +2.26321 +4.23587 +6.0418 +7.6838

14 +0.103215 +2.4344 +4.55621 +6.5012 +8.2692

15 +0.104425 +2.60298 +4.8744 +6.9594 +8.8539

16 +0.10438 +2.76438 +5.19438 +7.41638 +9.43438

17 +0.10298 +2.93085 +5.50298 +7.8719

+10.0189

18 +0.100085 +3.10085 +5.825 +8.3268 +10.599

19 +0.09548 +3.2554 +6.13848 +8.7784 +11.17748

20 +0.089085

+3.419 +6.4506 +9.22908 +11.749

Table (10) Net Cost of Internal Inspection after Subtracting the Depreciation Values and the rate of equipment damage



20

5. However, it may represent a considerable cost regarding

the running and maintenance cost. Therefore, the

proposed technique facilitates the internal predictive

inspections to reduce the process cost and enable the

secure operation of electrical equipment.

6. Internal inspections cause insignificant cost with respect

to the total capital cost of equipment. If the

equipment operating life is 20 years for example, the

total internal inspection cost is about 0.91124% of the

capital cost, saving about 6.27256% regarding the

external inspections omitting the extension in the

operating life due to the predictive maintenance.

7. Taking the extension in equipment life caused by

predictive maintenance into account, the total cost of

inspections can be compensated resulting in a net

saving, e.g. instead of paying about 0.91124% of the

fixed cost, 10.74876% of the fixed cost is earned

assuming 20% extension in the equipment life.

8. Taking the rate of equipment damage into account, the

total cost of inspections can be compensated resulting

in a net saving, e.g. instead of saving about

10.74876% of the fixed cost, 11.749% of the fixed

cost is earned assuming 20% extension in the

equipment life.

9. With more extension in equipment life, more savings can

be achieved due to the predictive inspections. Wrong

estimation of this extension in equipment life can be

misleading and can cause incorrect decisions.

10. Briefly, the proposed internal inspection technique can

provide economic as well as technical advantages

that encourage the wide utilization of predictive

maintenance.

Thus, the introduced research can help to extend the

work in the following directions:

1. The proposed technique can be applied to other electrical

power equipment (e.g. contactors, transformers, etc.).

2. With some modifications on the proposed program and

using online camera, the program can give proper

indication for the equipment instantaneously, e.g. alarm

or tripping systems.

3. A separate study can be carried out to accurately define

the time required for internal and external inspections for

different applications.

4. The program roles can be developed based on Expert

Systems.

REFERENCES

[1] S. A. M., and R. M. Nelms ''Diagnostic Technique for

Power Systems Utilizing Infrared Thermal Imaging'',

IEEE Transaction on IndustrialElectronics ,

Vol.,42,No.6,pp.615-628,December 1995.

[2]W. K W., P. N. Tan, K. L. and S. Lim ''Machine condition

Monitoring Using Omni directional Thermal Imaging

System'', IEEE InternationalConference on Signal and

Image Processing Applications,pp.299-304,2009.

[3] www.flirsystems.com

[4] W.K.W., P.N.Ta., C.K.Loo, and W.S.Lim'' An Effective

Surveillance Acquisition and Processing (ICSAP), , Kuala

Lumpur, Malaysia, pp.13-17, 3-5 Apr 2009.

[5] S. El Sh., M. F., R F. ''Thermal Image Prediction by Using

An Infrared Thermometer'', 13th International Conference

on Aerospace Sciences & Aviation Technology, ASAT-

13Paper: ASAT-13-TH-01,pp.1-8, May 26– 28, 2009.

[6] R.A., N. N. ''Measurement and Analysis of Temperature

Rise caused by Handheld Mobile Telephones using

Infrared Thermal Imaging'', IEEEInternational RF And

Microwave Conference Proceedings, Kuala

Lumpur,Malasya,pp.268-273, December 2-4, 2008.

[7] C. L. Heny, M. Frize ''Digital Processing Techniques For

The Assessment OF Pain With Infrared Thermal

Imaging'', Proceedings of the Second Joint EMBS/BMES

Conference Houston, TX TX, USA,pp.1157-1158,

October 23- 26,2002.

[8] J. R. '' Representation of Thermal Infrared Imaging Data in

the DICOM Using XML Configuration Files'',

Proceedings of the 29th Annual International Conference

of the IEEE EMBS Cite International, Lyon, France

August 23-26,pp.258-262, 2007.

[9] A. S., K. Ch. ''Robust Method For Analyzing The Various

Speeds Of Multitudinous Vehicles In Nighttime Traffic

Based on Thermal Images'', Fourth International

Conference on Computer Sciences and Convergence

Information Technology,pp.467-472,2009.

[10] J.L. Vavrin, W.T. Brown, M.R. Kemme, J. Westerman,

R. Lorand, C. Walden and C. Swinehart, Preventative

maintenance and reliability study for the central heating

and power plant at Fort Wainwright, Alaska,

ERDC/CERL TR-07-35, September 2007

[11] M. You, L. Li, G. Meng and J. Ni, Cost-effective updated

sequential predictive maintenance policy for continuously

monitored degrading systems, IEEE Transactions on

Automation Science and Engineering, Vol. 7, No. 2, April

2010, pp.257-265

[12] Y. Chou and L. Yao, Automatic diagnosis system of

electrical equipment using infrared thermography, 2009

International Conference of Soft Computing and Pattern

Recognition, pp.155-160, 2009

[13] C.A. Laurentys, A.P. Braga and S. Nascimento,

Intelligent thermographic diagnostic applied to surge

arresters: a new approach, IEEE Transactions on Power

Delivery, Vol. 24, No. 2, April 2009, pp.751-757

http://www.flirsystems.com/



22

[14] M. Shawal and S. Taib, Recent progress in diagnosing the

reliability of electrical equipment by using infrared

thermography, Infrared Physics & Technology, S1350-

4495 (12) 00025-4, 16 September 2011.

[15] F. Selim, I. Bedir and H. El desuoki, "Power cable

inspections using Matlab graphical user interface aided by

thermal imaging", 5th

Malaysian Conference in Software

Engineering (MYSEC), December 2011, pp. 263-268,

Johor Bahru, Malaysia .

[16] F. Selim, Ahmed M. Azmy, I. Bedir and H. El desuoki,

"Power cable inspections using Matlab graphical user

interface aided by thermal camera", 8th

ICEENG

Conference, 29-31 May, 2012, EE029, Cairo, Egypt.

[17] B.R.Gupta Generation Of Electrical Energy, Eurasia

Publishing House, 2002,pp.44-45

[18] J. Martinez, R. Lagioia, Experience performing infrared

thermography in the maintenance of a distribution utility,

19th International Conference on Electricity Distribution,

Vienna, 21-24, May 2007, Paper 0279.

[19] F. Lizak, M. Kolcun, Improving reliability and decreasing

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ActaElectrotechnica et Informatica Vol. 8, No. 1, ISSN

1335-8243 2008 FEI TUKE , pp.60–63.

[20]F. Selim, Ahmed M. Azmy and H. El Desuoki "Economic

Investigation of High Quality Thermal

Inspections",Proceedings of the 15th International Middle

East Power Systems Conference (MEPCON’12),

Alexandria University,Egypt, December 23-25, 2012,

Paper ID 229.

[21] F. Selim, Ahmed M. Azmy and H. El Desuoki

"Economic Investigation of High Quality Thermal

Inspections",Copyright © JES 2013 on-line :

journal/esrgroups.org/jes.

First A. Author

I. Taha was born in kafr-Elsheikh, Egypt

in 1972. He has graduated from Tanta

University, Egypt in 1995. He joined as a

demonstrator at the Faculty of

Engineering, Tanta University, Egypt, in

1996, and th en he obtained his M.Sc

degrees in 2000 from Mansura University

and the obtained his PhD. in 2007 from Tanta University. He

is currently an assistant professor at Electrical Power and

Machines Department at Tanta University (Today He is

assistant professor at Electrical Engineering at Taif

University). His research interests lie in the area of HVDC

power system, power electronics, electric motor drives and

solar energy.

Second Author

Ahmed M. Azmy was born in El-

Menoufya, Egypt. He received the B.Sc.

and M.Sc. degrees in electrical

engineering from the El-Menoufya

University, Egypt in 1991 and 1996,

respectively. He received the Ph.D.

degrees in electrical engineering from the

university Duisburg-Essen, Germany in

2005. He is the head of department of Electrical Power and

Machines Engineering- Tanta University, director of the

automated library project in Tanta University- director of the

quality assurance unit and executive director of the continuous

improvement and qualification for accreditation program. His

research topics are directed to the intelligent techniques,

dynamic simulation, smart grids, distributed generating units

and renewable energy resources. Dr. Azmy has been awarded

Tanta University Incentive Awards (2010) and Prizes for

international publishing in 2010, 2012 and 2013.

Third C. Author

F. Selim was born in kafr-Elsheikh, Egypt

in 1974. He has graduated from

Alexandria University, Egypt in 1997. He

work in many international companies,

and then he obtained his M.Sc degrees in

2007 from Alexandria University. He

joined as a demonstrator at the Faculty of

Engineering in 2009, and the obtained his

PhD. in 2012 from Tanta University. He is currently an

lecturer at Electrical Power and Machines Department at Kafr

Elshiekh University. His research interests lie in the area of

HV power system, economic in generation power, predictive

maintenance and solar energy.

Documents

High Quality Circuit Breakers Thermal Inspectionsresearchpub.org/journal/jac/number/vol2-no2/vol2-no2-3.pdf · Section three (right-side): indicates the main report (results), thermal-digital