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c ARISER Vol. 4 No. 3 (2008) 103-117 Engineering: In&Ct

Arab Research Institute in Sciences & Engineering

http://www.arabrise.org

Online Publishing Group ISSN 1994-3253

A Predictive Maintenance Tool: Vibration Analysis to Determine theCondition of Electric Machines

Assil SMEISMEH, Rayyan SAKLAWI and Wissam YASSINE

Electrical and Computer Department, American University of Beirut, LEBANON

{[email protected]; [email protected]; [email protected]}

Received 06 June 2008; Accepted 23 June 2008

In recent years, induction motors are becoming the most important equipment in industry. Motors fail-

ures often happen when they are needed the most. In some industries this cant be tolerated because itleads to catastrophic losses in production time, cost of repairing, and it indulges the safety conditions of

the industry. Studies have shown that defects in bearings are the main cause of failure of induction motor.

Predictive maintenance technique determines the condition of inservice machine by performing periodic

or continuous equipment monitoring. This approach offers cost savings over routine or time-based main-

tenance because tasks are carried out only when warranted. Predictive maintenance has many benefits. In

fact, it predicts failures before they occur and schedules down time repairs. There are several techniques

that can be employed to predict the condition of an electric machine, these include: vibration monitoring,

Current Signature Analysis, Tribology, Thermography, etc... Several studies showed that the most impor-

tant technique in predictive maintenance is vibration analysis as it gives clear indications regarding the

condition of the machine in question, in addition the level of vibrations and the frequency at which these

vibrations occur can serve in determining the exact location of the defect and possibly severity of such de-

fect. The theory behind using vibration analysis is that the presence of a fault in industrial equipment will

be accompanied by a detectable increase or modification of vibratory signal. The objective of our project is

to design, implement and test a predictive maintenance tool that uses vibration analysis to predict failures

in induction motors. An accelerometer will be used as a sensor to measure the vibration levels of the motor

being tested; a data acquisition card will collect these signals, filter them and convert them to digital signals.

The signals then will be converted to the frequency domain by FFT, after that we can analyze this spectrum

to determine the condition of the motor, and based on this evaluation it can be determined whether to keep

the motor in operation or if it requires maintenance. This can be a periodic action which can be carried out

without stopping the operation of the motor, and a log of the vibration levels can be saved to have a history

of the machine condition and capture important data regarding trends of the motor condition.

Contents

1 Introduction . . . . . . . . . . . . . . . . . 103

2 Predictive maintenance . . . . . . . . . . . 1043 Design of the predictive maintenance tool . 105

3.1 Theory behind vibration analysis . . . 105

3.2 Design . . . . . . . . . . . . . . . . . 107

4 Experimental work . . . . . . . . . . . . . 1085 Conclusion . . . . . . . . . . . . . . . . . 114

1 Introduction

Induction motors are AC motors that supply power to a rotating device by induction. An induction motor has a

stator (which represents the primary side of a transformer) and a rotor (which is the secondary side in a trans-

former). Thats why such a motor is called a rotating transformer. Induction motors are widely used, especially

polyphase induction motors, which are frequently used in industrial drives due to their rugged construction, the

lack of brushes and the ability to control the speed of the motor using power electronics. {Carnero [2]}.Used in severe conditions and with continuous operation, induction motors are at any times subject to failure

due to defects in its internal parts. Two separate studies where conducted by IEE and EPRI {Kumar & Manuhar

[4]} to determine the cause of failure, and the results are summarized in Tab. 1.

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104 A. Smeismeh et al.

FIG . 1: A simplified diagram of an induction motor {Integrated publishing [9]}.

TAB . 1: Distribution of motor failure according to part {Kumar & Manuhar [4]}

Failure IEE EPRIBearings 44% 41%

Stator 26% 36%

Rotor 8% 9%

Others 22% 14%

Its evident that defects in the bearings represent the widest source of failure to an induction motor, thus more

focus was needed on bearings defects in particular. Bearings main function is to reduce the friction and wear on

the rotating and stationary parts of the motor. In addition, the motor bearings support the output shaft. Motor

bearings have different types, like: roller bearings, ball bearings, sleeve bearings and needle bearings. {Grundfos[3]}. Its difficult to detect the exact reason of a bearing failure. The following is a list of the common defect causes:

{Grundfos [3]} Ordinary wear;

Too high ambient temperature;

Corrosion;

Reduced lubrication;

Misalignment;

Vibrations;

Damage due to transport;

Bearing currents from frequency converter drive.

Our predictive maintenance tool will act as a monitoring device, where vibration analysis will be used to

determine the presence of such defects in the bearings and perform the required maintenance before it effects the

operation of the motor.

2 Predictive maintenance

Predictive maintenance is a maintenance policy in which selected physical parameter associated with an operating

machine are sensed, measured and recorded intermittently or continuously for the purpose of reducing, analyzing,

comparing and displaying the data and information so obtained for support decisions related to the operation and

maintenance of the machine {Carnero [2]}. This will provide an increase in safety, quality and availability in theindustrial plants. Predictive maintenance had proved to be very effective through the years.

Some of the main of advantages of having a predictive maintenance program are: {Carnero [2]}

Exclusive control of the machines that show the beginning of a malfunction.

An increase in the availability of the industrial plants.

The capacity to carry out quality checks of both internal and subcontracted maintenance intervention.

An increase in the security of the workplace. Production quality is optimized by operating machinery without interruption due to failures.

Reduction of direct maintenance costs by checking only the equipment that is developing a fault.

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A Predictive Maintenance Tool: Vibration Analysis to Determine the Condition of Electric Machines 105

There are many techniques used in predictive maintenance programs: such as oil & wear analysis, motor circuit

analysis, vibration analysis, motion current signature and infrared

thermography (by temperature measurements) {Kumar & Manuhar [4]}. Several studies had shown the suit-ability of vibration analysis for application to rotary and reciprocating machines, which can be considered to bethe most widely used in general, in addition to its high capacity of diagnosis, make it the most versatile predictive

technique.

This technique consists of measuring the vibration levels from a certain machine and analyzing these measure-

ments to predict any failure in the machine. The presence of a fault in industrial equipment will be accompanied

by a detectable increase or modification of vibratory signal {Carnero [2]}, this is the main reason behind usingvibration monitoring to detect machine failure.

3 Design of the predictive maintenance tool

3.1 Theory behind vibration analysis

We will carry out a design of predictive maintenance tool that can sense vibrations of the bearing of an inductionmotor and analyze them in such a way to detect specific defects in the bearing which will lead to motor failure.

Each different bearing has unique characteristic rotational defect frequency which depends on the kinematics

consideration, this frequency can be calculated knowing the geometry of the bearing and its rotational speed,

usually these frequency lie in the low-frequency range (less than 500Hz). {Tandon & Choudhury [7]; Tandon etal. [8]}.

FIG . 2: Rough defect condition in a bearing {Al-Ghamda & David Mbab [1]}.

When a certain defect is present on a bearing element (example of a rough defect is shown in Fig. 2), an

increase in the vibration levels at this frequency can be noticed, and thats why frequency-domain analysis of

vibration reading is usually carried out to determine the condition of motor bearings.

Frequency-domain or spectral analysis of vibration signal is the most widely used approach for bearing defect

detection {Tandon & Choudhury [7]}.Usually Fast Fourier Transform (FFT) is applied to the vibration signal which converts it into a series of discrete

frequency components. In a frequency domain plot the X-axis in the frequency and the Y-axis is the velocity of

displacement {Mobley [5]}.

When a certain defect is present on a bearing element, an increase in the vibration levels at this frequencycan be noticed, thats why frequency-domain analysis of vibration reading is usually carried out to determine the

condition of motor bearings.

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After measuring the vibration signals and using FFT to transform it in to frequency domain we have to study it

carefully, especially the rotational defects frequencies of the bearing element, these frequencies can be calculated

by the following formulas {Tandon & Choudhury [7]}:Cage frequency

c =c

2

1

d

Dcos

(1)

Ball spining frequency:

b =Ds

2d

1

d2

D2cos2

(2)

Outer race defect frequency:

od = Zc =Zs

2

1

d

Dcos

(3)

Inner race defect frequency:

id = Z(s c) =Z

s2

1 +d

Dcos

(4)

Rolling element defect frequency:

re = 2b =Ds

d

1

d2

D2cos2

(5)

Where s w is the shaft rotation frequency in rad/s, d is the diameter of the rolling element, D is the pitch diameter,Z in the number of rolling elements and is the contact angle, as mentioned earlier these frequencies are usually

less than 500Hz.In a study to test vibration monitoring techniques published in the Mechanical Systems and Signal Processing

Journal in 2007 {Tandon et al. [8]}, a group of researchers calculated-using equation 3- the outer race defectfrequency to be 84.15Hz, they had a shaft rotating frequency of 24Hz, a test bearing having 9 balls of diameter

8.5mm and pitch circle diameter of 38.5mm with contact angle of 0. They measured the vibration signals andused FFT to get the frequency response; first with a healthy bearing then with a bearing with an outer race defect

of 1500m. By comparing the amplitude of vibration at the outer race defect frequency of 84.15Hz, a significantincrease in amplitude was observed as can be seen in Figs. 3 and 4. [8]

FIG . 3: Spectrum of vibration velocity of healthy bearing [8].

In addition to other results, these show the strong capabilities of vibration signature monitoring in determiningdefects in motor bearing, and based on the specific frequency at which the velocity amplitude increase we can

determine whats the nature of the defect and how severe its.

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FIG . 4: Spectrum of vibration velocity for 1500m defect [8].

3.2 Design

Our predictive maintenance tool should measure vibrations signals from the bearing of the motor, collect these sig-

nals, transform them into an analyzing interface where we can perform FTT and use the analysis method described

in the previous section.

The first and essential part of our tool is the sensing element, for this purpose we will use an accelerometer, the

accelerometer then transforms the data through a data acquisition card into a computer system at which LabVIEW

is installed and then we can perform any kind of analysis on the vibration signal we collected.

Fig. 5 is a flowchart that shows the basic operation of the predictive maintenance tool, next we will describe

briefly every component of this tool and how we choose the specific components.

FIG . 5: The components of our predictive maintenance tool.

Induction Motor: we are testing our design on a 1.5hp, 1400rpm, siemens induction motor. To study how vibrationis effected by different defects that might occur in bearing elements, we will record vibration signature when

running the motor with healthy bearing, then we will record the vibration signature when using defected

bearing, we will intentionally introduce several forms of defects to the bearing to study how each defect

changes the frequency spectrum of the bearing, this method was introduced in, several techniques to defect a

bearing include: acid etching, spark erosion, scratching or heating {Tandon & Choudhury [7]}.Accelerometer: The basic part of our vibration monitoring tool, its an electromechanical device that measures

acceleration forces, these might be static or dynamic forces caused by moving or vibrating objects.

The accelerometer gives a voltage reading that corresponds to the level of vibration, the accelerometer we areusing has a 1000mV/g sensitivity which means that for every 1000mV change in the voltage reading there is a1g(9.8m/s2) change in the acceleration of the object being sensed.

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Sensitivity, frequency range and other factors decide what kind of accelerometer is suitable for each purpose,

for our project we need an accelerometer that can be mounted on the bearing housing of the induction motor, since

we found that most of defect frequencies we will monitor are bellow 500Hz we dont need an accelerometer witha high frequency range.

We are using a Bruel & Kjaer Type 4507B accelerometer, which is a 1000mV/g, 2Hz5KH z frequency rangeand +7g measuring range. We mounted the accelerometer on the motor using wax, which is a very efficient andfast way of mounting, especially when the monitoring of several machines is required.

The accelerometer operation required having an ICP mode in our data acquisition card-DAQ, this is a constant

current supply to the accelerometer (220mA), and the output is modulated on the same line. Since the availableDAQ didnt have an ICP mode, we had to build our own constant current supply; the circuit of such supply is

shown in Fig. 6, as provided by the manufacturer of the accelerometer.

FIG . 6: Circuit of the current supply required to power the accelerometer.

Data acquisition card: The data acquisition card collects the data sensed by the accelerometer and converts this

analog signal into a digital signal that can be read by the computer, in addition it performs important processing

in the signal such as amplifying the signal and applying special filters.

We used a PCI-6040E DAQ, manufactured by National Instruments NI, this is important because they have a

special toolkit (Sound and vibration toolkit) which runs on LabVIEW and will be very useful for us in analyzing

the vibration signal.

LabVIEW: NI the manufacturer of our DAQ created a toolkit which runs on LabVIEW, this toolkit is called: NI

Sound and Vibration Toolkit, and AUB already have a license for using this toolkit.The toolkit provide enormous analyzing techniques that would help us manipulate our signal and get the most

out of it, we noticed a certain feature called zoom FFT which allowing analyzing the frequency range of interest

with improved resolution, and several other features that would help us better understand our data in a better way.

4 Experimental work

We started the experimental work in the spring term; the first step was to start a working station, it consisted of a

computer, a DAQ, an induction motor and an accelerometer.

The accelerometer we are using arrived late March, and we are still in the process of building the required

current supply circuit, this section will describe our experimental work so far.

The first step was to attach the DAQ to the computer, and then we installed LabView and all the additional

toolkits, including the vibration and sound toolkit on the computer. We wanted to test the DAQ, but since we didnthave the accelerometer ready, we started measuring generated signals from a function generator, thus we checked

the operation of the DAQ and its accuracy.

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Fig. 7 shows the apparatus setup of our experiment, it shows the PC, the junction box used to connect the

accelerometer to the DAQ, the motor we experimenting with the accelerometer mounted on its outer shell.

FIG . 7: Apparatus setup of the experiment.

We did not want to wait tell getting the required accelerometer to start designing the analysis procedure withLabView, thus we obtained an available Vernier 3-axis accelerometer, which wasnt appropriate for vibration

analysis since it had a 100Hz range and a +3g measure limit.We used this accelerometer to generate signals and measure them using the DAQ and perform the analysis on

LabView.

We viewed the time signal of the vibration using LabView; in addition we performed FFT on the signal and

viewed the frequency spectrum. We used a special VI in the vibration and sound toolkit called zoom FFT which

allows us to focus the spectrum on a particular range of frequencies, this is important because in our analysis its

required to focus on the rotational defect frequencies explained earlier. In addition we set a special test feature,

which could determine if a specific signal exceeds a specific limit which can be pre-determined or fed from one of

the parameters from the vibration signal. Upon having the actual signal from our accelerometer we might need to

add further analysis techniques, but at this level the available techniques are sufficient. Fig. 8 shows a screenshot

of the analysis outcome on LabView.Mainly our algorithms of detecting a failure in the bearing of a motor we are currently monitoring consists of

measuring the overall vibration level in time domain, and compare it with the average level of vibration while at

normal operation, this would serve as one data point. Next we will turn our attention into the frequency domain, the

overall spectrum will be observed for any apparent change in the level of vibrations, and then the defect frequencies

which we will calculate in the next section will be examined with special attention, we expect that vibration levels

at these frequencies will be the most indicating and will help us diagnosis the defect with more precision, as

identifying the cause of defect and approximating the size and severity of such defects.

Finally we turned our attention into the induction motor, we opened the motor and got the bearings, we had to

calculate the dimensions of the bearings to get the rotational defect frequencies as the formulas to calculate these

frequencies depend of the geometry of the bearing and rotational speed of the motor. The bearings turned out to be

of the type 6205zz, and its dimensions are shown in Fig. 9:

Using these data we were able to calculate the defect frequencies, we had the following given:

Shaft frequency = Fs = RPM/60 = 23.3HzNumber of balls = Z = 9

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FIG . 8: Screenshot of the results of the analysis in LabView.

FIG . 9: Scheme and dimensions of the 6205 ball bearing.

Diameter of the rolling element = 10mmPitch diameter = 38mmThe defect frequencies where calculated:

Fc = 8.58Hz

Fb = 41.2Hz

Fod = 77.26Hz

Fid = 132.4Hz

Fre = 82.4Hz

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We bought 3 bearings of the same type to perform our experiment on them. Several methods are available to

defect the bearings, one is to heat the outer race which we did on one of the bearing, and we will study the effect

of such defect on the frequency spectrum, and more defects will be introduced later on to study the effect of eachparticular defect.

FIG . 10: Defected Bearings.

When a certain defect is present on a bearing element (example of a rough defect is shown in Fig. 30), an

increase in the vibration levels at this frequency can be noticed, and thats why frequency-domain analysis of

vibration reading is usually carried out to determine the condition of motor bearings.

When we build the current supply for the accelerometer, then we are able to start our analysis using the ac-

celerometer.

FIG . 11: The full LabView program.

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Fig. 11 above shows our LabVIEW program after fully adding all the VIs required for our project. Here we

see the block diagram of all the functions, while Fig. 12 shows the front panel where all the waveforms will be

displayed.

FIG . 12: Front Panel showing our results.

Several steps are required to determine the origin of the defect and thus apply the needed maintenance on that

part and enhance or restore the motors operating condition to good and normal.

The first step of our analysis is to check the time domain vibration levels, and compare them with predetermined

normal operation level of such motor, for our motor we ran several tests to determine the vibration at normal level,

and we determined that vibration levels above 1.7G would be considered as an alert and provoke the need forfurther analysis. Fig. 13 shows the time domain vibration level.

FIG . 13: Vibration analysis waveform in time domain of healthy bearing (Amplitude in g and time in sec).

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The vibration level reaches the maximum of 0.9G, which confirms that the motor is running normally withoutany defects.

Next we will carry out a frequency domain analysis; Fig. 36 shows the same signal after applying FFT:

FIG . 14: Vibration analysis waveform in frequency domain of healthy bearing (Amplitude in cm/sec and frequency in Hz).

The frequency spectrum shows that the motor vibrates at maximum velocity at around 280Hz with velocity of

0.08cm/sec this complies with the standards explained in the section 10.2 and summarized in table 6 that state thata good operating motors vibration should be 0.049cm/sec and bellow, this confirms our initial diagnosis-based onthe time domain analysis- that the motor is operating normally and no further action is required.

Next, we a start testing and collecting vibration signature of a motor with defected bearing. We need to monitor

the vibration levels of the motor, and compare them with the normal operation levels. we will always start our test-

ing procedure with checking the time domain vibration levels, then the frequency domain levels, and if necessary

further inspection will be carried out using the zoom frequency analysis which allows more insight on the nature

of the defect.

In the case of the defective bearing, a detectable increase in vibration levels will result. Fig. 15 shows the

waveform of the time domain vibration level:

In the above waveform its apparent that the vibration exceeds the normal level which calls for immediate

inspection from a specialist to determine the fault.

Now, we apply FFT and checking the vibration level in frequency domain against the alert limits.

In the case of the defected bearings, Fig. 18 shows how the program indicates that the vibration exceeded the

limit:

It is clear that the function indicates the points at which the vibration level exceeds the alarm limit of 178cm/sand thus the limit test fails indicating the need for further analysis which will be carried out next in the zoom

analysis.

We will compare the frequency waveform of the healthy and the defected bearing, and pay certain attention to

the vibration level at 77Hz since its the outer race defect frequency, and defects where introduced to this section

of the bearing, Fig.s 19 and 20 compare the vibration level at this frequency.

After inducing the defect, the vibration level at the defect frequency = 77Hz becomes 0.09. (Thus increasingby 4 times)

This confirms that the increase in vibration analysis was caused by a defect in the outer race, the increase in thelevel of vibration at other frequencies is because at the shops several other defects where introduced to the motor

which made it hard to separate the increase due to different defects.

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FIG . 15: Vibration analysis waveform in time domain of defected bearin (Amplitude in cm/sec and frequency in Hz).

FIG . 16: Limit test in time domain of defected bearing. (Amplitude in g and time in sec).

The frequency spectrum of the vibration signal from outer race defect shows the peaks at 77Hz, 144Hz, 225Hz,275Hz, 350Hz, 425Hz and 475Hz. The fundamental frequency for the outer race defect bearing from equation (3)is found to be 77Hz.

Existing of multipliers of the cylindrical rolling bearing outer race defect frequency (77.26Hz) on the spectrumgraph and impact signals whose frequency is equal to outer race defect frequency (Wod), in the time domain

waveform graph indicate that ball bearing outer race has a defect. Since the magnitude of spectrum at various

harmonic frequencies for the defective bearing is found to be quite distinct in comparison to good bearings.

5 Conclusion

Our project was successful in determining the presence of a fault. The right choice on the accelerometer, data

acquisition card, and ICP circuit was an aid to accomplish a ready environment for reading vibrations and with

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FIG . 17: Vibration analysis waveform in frequency domain of defected bearing (Amplitude in cm/sec and frequency in Hz).

FIG . 18: Limit test in frequency domain of defected bearing (Amplitude in 103cm/sec and frequency in Hz).

TAB . 2: Frequency compositions of outer race defect for healthy and defected motor

Spectral Frequencies

Amplitude

(cm/secRMS)Healthy

Amplitude

(cm/secRMS)Defected

Corresponding

Fo = 77.26Hz 0.022 0.09 1F1 0.023 0.098 2F2 0.032 0.158 3F3 0.066 0.183 4F4 0.033 0.11 5F5 0.035 0.093 6F6 0.032 0.105 7

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FIG . 19: Vibration analysis zoom waveform in frequency domain of healthy bearing (Amplitude in 103cm/sec andfrequency in Hz).

FIG . 20: Vibration analysis zoom waveform in frequency domain of defected bearing (Amplitude in 103cm/sec andfrequency in Hz).

FIG . 21: Frequency spectrum of a healthy compared to an unhealthy bearing.

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the help of LabVIEW, further interpretation and analysis was possible. With the right apparatus, we could read the

vibration of any motor; see its waveform in time domain and even in frequency domain after applying Fast Fourier

Transform with the help of LabVIEW functions. The drastic and immense increase in vibration levels read firston the time domain and then confirmed in the frequency domain by known data standards that show the motors

operating condition, reveal the presence of a fault and the need for maintenance.

Our project seems perfect to detect a fault, however is a bit shaky in determining the exact origin of that

fault. Moreover, the defect frequencies computed by the equations deviate to some amount from obtained-ones by

measurement. This is because equations use shaft speed by declared manufacturer but real speed of the shaft may

be different from this speed in the moment of vibration measurement, in addition to human error in measuring the

dimensions of the bearing.

Moreover, our first time contact to vibration levels, LabVIEW and frequency analysis, in addition to the lack of

papers on similar techniques. In conclusion, our results and methodology seems satisfactory and providing results,

nevertheless future work is needed to be fully able to determine the exact source of the defect.

Time waveform and frequency spectrum provide useful information to analyze defects in antifriction bearings.

Time waveform indicates severity of vibration in defective bearings. Frequency domain spectrum identifies ampli-

tudes corresponding to defect frequencies and enables to predict presence of defects on inner race, outer race androllers of antifriction bearings. The distinct and different behavior of vibration signals from bearings with inner

race defect, outer race defect and roller defect helps in identifying the defects in roller bearings.

Also, it can be concluded that if vibration monitoring is applied within regular selected periods, capable in-

strumentation and if vibration analysis is performed by experienced personnel, impending failures can be easily

detected.

We would like to acknowledge the continues help and support from our supervisors Dr. Ali Chehab and Dr.

Fouad Mrad, they always had time to make sure we are in the right track, and placed their knowledge and experience

in our use. Also we want to thank the lab managers, Mr. Ghassan Deeb Mr. Khaled Joujou, Mr. Salam Abyad Mr.

Fuad Shihab for being helpful all the time whenever we needed any assistance.

References

1. Al-Ghamda A.M., Mbab D. (2006) A comparativeexperimental study on the use of acoustic emission

and vibration analysis for bearing defect identifica-

tion and estimation of defect size. Mechanical Sys-

tems and Signal Processing, 20(7), 1537-1571.

2. Carnero M.C. (2005) Selection of diagnostic tech-

niques and instrumentation in a predictive mainte-

nance program. A case study. Decision support sys-

tems, 38(4), 539-55.

3. Grundfos Motor Book, Motor bearings:

http://net.grundfos.com/doc/webnet/

professional_profile/pdf/7_Motor_

bearings.pdf4. Kumar P. & Manuhar V.H. (2003) Comprehensive

Predictive Maintenance of electrical motors in In-

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Nuclear Power, 17.

5. Mobley R.K. (1999). Vibration Fundamentals. El-

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in Proc. 28th Southeastern Symposium on System

Theory SSST96, 301.

7. Tandon N. & Choudhury A. (1999) A review of the

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detection of defects in rolling element bearings. Tri-

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8. Tandon N., Yadava G.S. & Ramakrishna K.M.

(2007) A comparison of some condition monitoring

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motor ball bearings. Mechanical Systems and Sig-

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