Report on Fault Diagnosis of Ball Bearing System

Fault Detection Of Bearings Using Signal Processing MATLAB
Dept. of Mechatronics, MITE Page 1
CHAPTER 1
INTRODUCTION
Detection of bearing faults is one of the most challenging tasks in bearing health
condition monitoring, especially when the fault is at its initial stage. The defects in bearing
unless detected in time may lead to malfunctioning of the machinery. The defects in the
rolling element bearings may come up mainly due to the following reasons; improper design
of the bearing or improper manufacturing or mounting, misalignment of bearing races,
unequal diameter of rolling elements, improper lubrication, overloading, fatigue and uneven
wear. Now a day worldwide engineers are focusing on the design and the material used for
developing machine and schedule the maintenance tasks to make sure the machine will work
until the maximum time. Moreover, by applying the concept of Condition Monitoring (CM)
techniques the running condition of the machine can be analyzed. The Vibration Monitoring
(VM) is the most commonly used analysis method to analyze the running condition of
machine and it may provide the clear identification of most of the faults in the machine.The
proposed fault diagnosis method is firstly tested on a test bed and then an online monitoring
and finally fault diagnosis system is designed for bearings. A multi sensor data collection
and Principal Component Analysis (PCA) are proposed to develop a framework for impeller
fault detection. In this report an experimental investigation has been carried out on Bearings
experimental setup to analyze the behavior of the system under various belt defects
conditions. The results were analyzed and presented.

CHAPTER 2
LITRATURE SURVEY
I. Dhirendra Nath Thatoi, Harish Ch. Das, and Dayal R. Parhi, “Review of
Techniques for Fault Diagnosis in Damaged Structure and Engineering System”
Focus has been made to give an overview of various methodologies used in fault
diagnosis and condition monitoring. A crack in vibrating structures can lead to premature
failure if it is not detected in time. Researchers have been working on the dynamics of
cracked structures for decades to be able to monitor a structure and diagnose fault at the
earliest possible stage. An effort has been made in the current paper to understand
different techniques and methodologies for fault diagnosis and condition monitoring of
damaged structures subjected to varied dynamic loading. The methods used are classical,
wavelet transform, and finite element methods, artificial intelligence methods, and
numerical and experimental methods. Using classical methods, engineers are able to
predict faults. But using artificial intelligence techniques, it is observed that the
forecasting time for fault diagnosis improves a lot in comparison to other methodologies.
II. Endo Hiroaki and Sawalhi Nader, “Gearbox Simulation Models with Gear and
Bearing Faults” Simulation is an effective tool for understanding the complex
interaction of transmission components in dynamic environment. Vibro-dynamics
simulation of faulty gears and rolling element bearings allows the analyst to study the
effect of damaged components in controlled manners and gather the data without
bearing the cost of actual failures or the expenses associated with an experiment that
requires a large number of seeded fault specimens. The fault simulation can be used to
provide the data required in training Neural network based diagnostic/prognostic
processes.
III. Dong Wang, Qiang Miao, Xianfeng Fan and Hong-Zhong Huang, “Rolling element
bearing fault detection using an improved combination of Hilbert and Wavelet
transforms.”As a kind of complicated mechanical component, rolling element bearing
plays a significant role in rotating machines, and bearing fault detection benefits
decision-making of maintenance and avoids undesired downtime cost.However,
extraction of fault signatures from a collected signal in a practical working environment
is always a great challenge. This paper proposes an improved combination of the Hilbert
and wavelet transforms to identify early bearing fault signatures. Real rail vehicle
bearing and motor bearing data were used to validate the proposed method. A traditional

combination of Hilbert and wavelet transforms was employed for comparison purpose.
An indicator to evaluate fault detection capability of methods was developed in this
research. Analysis results showed that the extraction capability of bearing fault
signatures is greatly enhanced by the proposed method.
IV. Liu ziran, He tao, Jiang guoxing, “Analysis of wavelet envelope spectrum to
vibration signal in the gearbox.” Based on the methods of wavelet analysis, taking the
gearbox CA6140 as a subject investigated, this paper introduces the application of
wavelet analysis and wavelet envelope spectrum in fault diagnosis field. Though
Daubchies wavelet decomposition and Hilbert envelope spectrum analysis, the failure
frequency of the vibration signal of the gearbox can be found out.
V. Arunkumar K.M., Dr. T.C.Manjunath, “A brief review/Survey of vibration signals
in time domain”Vibration signal analysis and monitoring is a predictive maintenance
technique that can detect the faults in the machines. In this paper, data acquisition
system, signal analysis and lab VIEW Tool is used for detecting the faults in machines.
Thus, preventive action can be taken in advance. For monitoring and analysis of
vibration signal, time domain, frequency domain and time-frequency domain analysis of
vibration signal is implemented. Wavelet transform analysis will give more accurate
information about the vibration signal type, signal fault region and fault extent as
compared to time domain analysis. In this paper, a brief review about the concerned
research work is presented & is just a survey / review paper & there is no novelty in it
and is only a collection of works done by various authors. This will surf as a base for all
the people who wants to pursue their career in the field of control systems.
VI. E. Pazouki, “Fault Diagnosis and Condition Monitoring of Bearing Using
Multisensory Approach Based Fuzzy-Logic Clustering.” Here investigates the
application of multisensor fault feature extraction and fuzzy-logic based clustering for
the condition monitoring of bearing. Multiple independent sensors on an electric motor
drive system provide valuable early indication of a fault, and can be effectively utilized
to perform high reliable and optimal fault detection. Through utilizing common sensors
including current sensor and vibration sensors in motor, motor current signature analysis
(MCSA) and vibration analysis have been used to extract the bearing fault energy. The
discrete wavelet transform (DWT) has been applied to monitor energy of the bearing
fault signals. Then, the fuzzy c-mean (FCM) has been developed to utilize the data from

single sensor and multisensor to identify the severity of bearing fault. Extensive
theoretical analysis and experimental test has been performed to demonstrate the
advantages of proposed approach.

CHAPTER 3
PROBLEM DEFINITION
Some of the most common Roller bearings symptoms and causes of failure are discussed
here to understand how to overcome these failure by taking proper troubleshoot so that
failure can be minimized. The most common faults are :
3.1 Excessive loads
Excessive loads usually causes premature fatigue. Tight fits, brinelling and improper
preloading also causes the same problem.
Fig.1. Excessive load bearing fault
3.2 Overheating
Symptoms are discoloration of the rings, balls, and cages from gold to blue.
Temperatures in excess of 400°F can anneal the ring and ball materials. The resulting
loss in hardness reduces the bearing capacity causing early failure. In extreme cases,
balls and rings will deform. The temperature rise can also degrade or destroy
lubricant
Fig.2. Due to Overheating of bearing
3.3 Loose fits
Loose fits can cause relative motion between mating parts. If the relative motion
between mating parts is slight but continuous, fretting occurs.

Fig.3. Fault due to loose fit
3.4 Roller balls fault
Generally this type of faults are common in the industries due to excessive loads on the
bearings which again causes more vibrations in bearings and also free movement will
not be there.
fig.4. roller bearing balls
3.5 Inner rays fault
This cause is due to improper alignment of bearings on the shaft in which it will be
placed.
Fig.5. Inner rays fault

CHAPTER 4
METHODOLOGY
The aim of the project is to study the different bearing conditions having faults. Thus
this required an electric motor along with two pulleys of same diameters and belts Along
with that sensors such as NI accelerometers along with a suitable data acquisition system to
record and study the vibrations.
To fulfill our objectives a proper setup is needed. A conceptual design is developed
with the project objective in mind. The project requires a source of mechanical energy in the
form of rotary motion, which is satisfied by the power unit comprising of an electric motor.
There is a requirement also for high precision sensors such as accelerometers to measure the
vibrations in single with high sensitivity.

With the help of all the components required the setup is fabricated accordingly using
the following components with their specifications are mentioned below :
Fig .6 Conceptual Design
Once the experimental setup is completed, using the same setup the vibration signals are
acquired using the DAQ card to which high sensitivity accelerometer is connected. Both
DAQ card and accelerometer are interfaced using LAB View software. Through signal
processing technique using MATLAB software faults are classified in the later stages of this
project.

4.1 COMPONENT SELECTION
4.11 MOTOR
 Capacity : 3-Phase, 440V, 50Hz
 Power : 0.37KW, 0.5HP
 Rated speed : 1100 RPM
 Rated current : 1.4Amps
 Amb. Temperature: 50° C
4.12 MOTOR STARTER
 Phase : 3
 Coil Voltage : 280/440
 Amperes : 15
 Cycles : 50
 Relay Range:13-21
4.13 PULLEY
Driver Pulley:
 Material used : Aluminum
 Outer Diameter: 100mm
 Inner Diameter : 19mm
 Type : “V” groove, Class “A”
Driven Pulley:
 Material used : Aluminum
 Outer Diameter: 100mm
 Inner Diameter: 19mm
 Type : “V” groove, Class “A”
4.2 TYPES OF BEARINGS
Ball Bearings utilize balls as the rolling elements. They are characterized by point
contact between the balls and the raceways. As a rule, ball bearings rotate very quickly but
cannot support substantial loads.
Ball bearings are available in various cross sections and satisfy a huge variety of
operating conditions and performance requirements.

Insert bearings (Y-bearings):
Insert bearings (SKF Y-bearings) are based on sealed deep groove
ball bearings in the 62 and 63 series, but have a convex outer ring
and in most cases an extended inner ring with a specific locking
device, enabling quick and easy mounting onto the shaft.
 Quick and easy mounting
 Accommodate initial misalignment
 Long service life
 Reduced noise and vibration levels
Angular contact ball bearings:
Angular contact ball bearings have inner and outer ring raceways that
are displaced relative to each other in the direction of the bearing axis.
This means that these bearings are designed to accommodate
combined loads, that is for simultaneously acting Radial and Axial
loads,
The axial load carrying capacity of angular contact ball bearings
increases as the contact angle increases. The contact angle is defined
as the angle between the line joining the points of contact of the ball
and the raceways in the radial plane, along which the combined load is
transmitted from one raceway to another, and a line perpendicular to
the bearing axis

Self-aligning ball bearings:
Self-aligning ball bearings have two rows of balls, a common
sphered raceway in the outer ring and two deep uninterrupted
raceway grooves in the inner ring. They are available open or sealed.
The bearings are insensitive to angular misalignment of the shaft
relative to the housing which can be caused, for example, by shaft
deflection.
 Accommodate static and dynamic misalignment,,
 Excellent high-speed performance
 Minimum maintenance
 Low friction
 Excellent light load performance
 Low noise
Thrust ball bearings:
Thrust ball bearings are manufactured as single direction or double
direction thrust ball bearings. They are designed to accommodate
axial loads only and must not be subjected to any
Radial load,
 Separable and interchangeable
 Initial misalignment
 Interference fit

Deep-Groove Ball Bearings:
Deep groove ball bearings are particularly versatile. They are
suitable for high and very high speeds, accommodate radial and
axial loads in both directions and require little maintenance.
Because deep groove ball bearings are the most widely used
bearing type, they are available from SKF in many designs,
Variants and sizes
 They absorb axial forces in both directions.
 low torque
 suitable for high speeds
4.3 FABRICATION
The experimental setup used for this study is designed and fabricated to collect
vibration data for different working conditions. It consists of the following parts were
selected and fabricated. The Motor of 0.5Hp and maximum speed of 1100 rev/min with
power rating capacity of 0.37 KW as shown in Fig. 9 is selected. Bearings (No. LS8) of 19
mm diameter shown in Fig. 10 are used to support shaft. Two pair of pulleys (driven with
outer diameter of 100 mm and inner diameter of 19 mm and driver with outer diameter of
100 mm and inner diameter of 19 mm) were used. The two pulleys are connected motor and
the other two pulleys are mounted on a shaft diameter of 15 mm shown in Fig.10 A V-Belt
(A-838, Ld 871) shown in Fig. 12 is used to connect driver and driven shaft. These units are
mounted on a strong wooden base.
Experiments were conducted at three load condition with no load at 955 RPM and at
half load at 877 RPM and full load at 750RPM.

Fig 7. Pulley with connection to motor
Fig.8 Pulleys with shaft Fig. 9 “V” Belt

Fig10.experimental setup
4.4 MODEL TESTING
LabVIEW14® (Laboratory Virtual Instrument Electronic Workbench NI-National
Instrument) application software model, developed (LabVIEW™ 7, 2014) with FFT analyzer
is used to acquire vibration signals data through four channel sensor input module Data
Acquisition Device (NI-DAQNational Instruments-NI ComapactDAQ™-9174 chassis
through NI cDAQ-9174-channel 0). The photographic view of LabVIEW® software
integrated with DAQ module and computer is shown in Fig. 14.

Fig. 11 LabVIEW® software integrated with DAQ module and computer
The unidirectional piezoelectric accelerometers (ICP® (IEPE) Accelerometer, IMI
sensors, sensitivity is 10 mV/g to 100 mV/g and frequency range up to 18 kHz) was used to
acquire vibration signals in vertical, horizontal and axial directions for the belt in healthy
condition as shown in fig. 15
Fig. 12 Accelerometer
These signals were later used to compare the signals with belts fault condition to identify the
cause.
The sensor continuously stores the vibration data from the ball bearing. This data is
analyzed using a MATLAB program. The purpose of developing MATLAB program is to
advance the existing technology and process data faster, But the program is not running
throughout the operation of the ball bearing and it is up to the user to run the program to
analyze the data and determine the condition of the ball bearing.

4.5 MATLAB PROGRAM
clc;
clearall
closeall
filename = 'D:ProjectBearing AnalysisReadingsBall FaultFull LoadBF
trial 02 FL 735 rpm.xlsx';
sheet=1;
xlrange='B1:B153600';
x=xlsread(filename,xlrange);
fs=10000;
t=1/fs;
L=length(x);
t=(0:L-1)/fs;
subplot(3,1,1)
plot(t,x)
gridon;
xlabel('Time (Sec)');
ylabel('Acceleration (m.sec^-2)');
title('Time Domain Signal')
Y(1)=0;
Y=fft(x);
freq = 0:fs/L:fs/2-fs/L;
freq = freq';
amplitude = abs(Y(1:floor(L/2)))/floor(L/2);
subplot(3,1,2)
plot(freq,amplitude);
xlabel('Frequency [Hz]');
ylabel ('Amplitude');
title('Amplitude Spectrum')
gridon
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
Y = fft(Y,NFFT)/L;
f = fs/2*linspace(0,1,NFFT/2+1);
% Plot single-sided amplitude spectrum.
subplot(3,1,3)
plot(f,2*abs(Y(1:NFFT/2+1)))
title('Freq Domain')
xlabel('Frequency (Hz)')
ylabel('|Y(f)|')
gridon
%Sin wave rms value will be gievn by 1/square root of 2,refers to line 21%
peaktd =((max(x)-min(x))/2); % Peak to peak value of a sin wave
considered
peak2peaktd=2*peaktd; % To get the total peak value it is
multiplied by 2times%
rms707peak=(0.707)*(peaktd);
y=mean(x); % all the values gets added and divide by total number of
readings that is 19 here%
%Variance term in Standard deviation - for simplification%

%standard devation formula and (rms formula same)%
%crest factor is defined as the ratio of peak value to the rms value%
x1=x';
peakvalue=max(x1)
rm_value=((sum((x1).^2))/L)^0.5
crestfactor=peakvalue/rm_value
X=mean(x);
r=1;
fori=1:175000;
s_ubtractred(r)=[x1(i)-X];
s_ub_skeeew(r)=(x1(i)-(X/rm_value));
r=r+1;
end
standard_deviation=sqrt((sum(s_ubtractred))/L)
Standard_Deviation=std(x1)
% s_kewness=sqrt(sum(s_ub_skeeew)/L);
shape_b=(sum(x1)/L);
shape_factor=sqrt(rm_value/(shape_b))
A. Healthy Condition
Initially experiments were conducted for three load condition one is at lower speed of
driven pulley at 1731 RPM whereas driver pulley rotates at 1877 RPM and other is at higher
speed of driven pulley at 1940 RPM whereas driver pulley rotates at 1940 RPM under good
operating condition without any faults in the belt. The corresponding speed is measured by
tachometer. Next using LabVIEW 2014 software the vibration signals in horizontal, vertical
and in axial directions were acquired. These signals were later used to compare the signals
with ball bearing fault condition to identify the cause.
B. Faulty Conditions
Two faults condition were created in the bearings to study and analyze the behavior of the
rotating system under different operating speeds.
 The first one is inner rays fault
 The second one is ball fault

CHAPTER 5
EXPERIMENTAL RESULTS AND GRAPHS
Readings obtained using LabVIEW Software
1. Ball fault with full load (3Kg)
Time Vibration Frequency Value
3.91E-05 -1.015448 1 0.019922
7.81E-05 -1.432923 2 0.035409
0.000117 -1.069026 3 0.038692
0.000156 -2.6377 4 0.022983
0.000195 -1.265343 5 0.009198
0.000234 -1.772732 6 0.019013
0.000273 -1.66095 7 0.020501
0.000312 -1.179304 8 0.009825
0.000352 -1.425287 9 0.020402
0.000391 -1.011711 10 0.012774
0.00043 -2.599152 11 0.00323
0.000469 -1.156323 12 0.008213
0.000508 -1.05347 13 0.011209
0.000547 -2.768129 14 0.005355
0.000586 -0.037923 15 0.004358
0.000625 -0.691388 16 0.022244
0.000664 -2.963816 17 0.016403
0.000703 -1.631881 18 0.009663
0.000742 -0.659 19 0.016111
Ball fault with full load
2. Ball fault with half load (1.5 Kg)
0 0.383385 0 0.022912
3.91E-05 1.90351 1 0.014799
7.81E-05 0.965744 2 0.004499
0.000117 0.810654 3 0.003015
0.000156 0.755171 4 0.002402
0.000195 1.486833 5 0.001465

Ball fault with half load
3. Ball fault with no load condition
0 -1.14618 0 0.024441
3.91E-05 -0.19715 1 0.01599
7.81E-05 -0.31738 2 0.002054
0.000117 0.449346 3 0.005974
0.000156 1.067762 4 0.006088
0.000195 -0.24496 5 0.004242
0.000234 -0.35457 6 0.005558
0.000273 0.556642 7 0.00672
0.000312 -0.86432 8 0.005535
0.000352 1.514409 9 0.003762
0.000391 0.454563 10 0.005444
0.00043 0.083412 11 0.00571
0.000469 -0.04128 12 0.005122
0.000508 -0.35791 13 0.005778
0.000547 0.468227 14 0.010288
0.000586 0.800068 15 0.004545
0.000625 1.878239 16 0.047328
0.000664 -0.54588 17 0.047322
0.000703 0.826507 18 0.014792
0.000742 -1.49177 19 0.020089
Ball fault with no load
0.000234 0.090395 6 0.002384
0.000273 -0.15838 7 0.003434
0.000312 1.287857 8 0.00234
0.000352 0.58186 9 0.004136
0.000391 -0.04989 10 0.003403
0.00043 1.107048 11 0.000493
0.000469 -0.79869 12 0.001766
0.000508 -0.24309 13 0.002572
0.000547 -0.10725 14 0.003389
0.000586 1.804521 15 0.004707
0.000625 -0.11159 16 0.025939
0.000664 -0.01092 17 0.024315
0.000703 0.310889 18 0.001241
0.000742 1.617225 19 0.009468

4. Inner rays fault with full load (3Kg)
0 -6.69761 0 0.004936
3.91E-05 1.124273 1 0.006051
7.81E-05 -1.13079 2 0.008306
0.000117 -5.23791 3 0.009069
0.000156 3.058421 4 0.005212
0.000195 0.369806 5 0.00231
0.000234 -3.25332 6 0.005047
0.000273 0.609544 7 0.008181
0.000312 -1.18387 8 0.011562
0.000352 3.500237 9 0.018314
0.000391 -3.55592 10 0.016898
0.00043 -4.11039 11 0.004697
0.000469 -4.46227 12 0.005444
0.000508 0.692469 13 0.006939
0.000547 -1.09394 14 0.008971
0.000586 -3.03955 15 0.003597
0.000625 -1.93826 16 0.009829
0.000664 -2.55675 17 0.007631
0.000703 -1.07645 18 0.013842
0.000742 -1.2495 19 0.025021
Inner rays fault with full load
5. Inner rays fault with half load (1.5Kg)
0 3.99065 0 0.004159
3.91E-05 -2.25533 1 0.007489
7.81E-05 -3.27412 2 0.009595
0.000117 3.010659 3 0.015649
0.000156 2.771695 4 0.018368
0.000195 -2.5629 5 0.011595
0.000234 0.872407 6 0.006707
0.000273 1.349785 7 0.006536
0.000312 -0.07346 8 0.008336
0.000352 2.587096 9 0.016178
0.000391 0.770256 10 0.011424
0.00043 1.687388 11 0.003364
0.000469 -5.17275 12 0.008111
0.000508 -3.95185 13 0.008702

0.000547 4.257921 14 0.008163
0.000586 -2.58354 15 0.003434
0.000625 2.55848 16 0.024413
0.000664 -11.7017 17 0.023942
0.000703 -4.6334 18 0.014738
0.000742 6.572054 19 0.022065
Inner race fault with half load
6. Inner rays fault with no load
0 -3.58273 0 0.00668
3.91E-05 2.370834 1 0.005947
7.81E-05 2.761961 2 0.002248
0.000117 -1.35488 3 0.003807
0.000156 4.22927 4 0.004389
0.000195 -1.72687 5 0.00652
0.000234 -2.15064 6 0.013026
0.000273 -0.82589 7 0.009418
0.000312 6.177033 8 0.004705
0.000352 -2.39285 9 0.009376
0.000391 -1.83198 10 0.006381
0.00043 -1.80988 11 0.007969
0.000469 -4.74028 12 0.012644
0.000508 -0.39885 13 0.008137
0.000547 1.850663 14 0.006802
0.000586 1.676681 15 0.013336
0.000625 2.313452 16 0.029787
0.000664 -2.86845 17 0.030334
0.000703 -1.4592 18 0.009221
0.000742 4.384184 19 0.002189
Inner race fault with no load
7. Test bearing with full load (3Kg)
0 0.707209 0 0.005157
3.91E-05 0.582114 1 0.004589
7.81E-05 0.187147 2 0.006052
0.000117 -0.20507 3 0.007039
0.000156 -0.63197 4 0.006263

0.000195 -1.02819 5 0.005081
0.000234 -1.49491 6 0.008235
0.000273 -1.65335 7 0.006719
0.000312 -1.76475 8 0.004443
0.000352 -1.78255 9 0.008807
0.000391 -1.82732 10 0.01226
0.00043 -1.80349 11 0.006764
0.000469 -1.10162 12 0.007081
0.000508 -1.01662 13 0.009777
0.000547 -1.25641 14 0.005393
0.000586 -0.6747 15 0.005555
0.000625 -0.75145 16 0.023051
0.000664 -1.01552 17 0.019453
0.000703 -0.92255 18 0.00651
0.000742 -0.91691 19 0.000214
Test bearing with full load
8. Test bearing with full load (1.5Kg)
0 0.433082 0 0.013778
3.91E-05 0.477145 1 0.009374
7.81E-05 0.379461 2 0.001005
0.000117 0.281844 3 0.000578
0.000156 -0.00509 4 0.002305
0.000195 0.061157 5 0.001917
0.000234 0.269474 6 0.000902
0.000273 0.087874 7 0.00188
0.000312 0.128139 8 0.002559
0.000352 0.307987 9 0.005687
0.000391 -0.01122 10 0.005379
0.00043 -0.54333 11 0.002666
0.000469 -0.98045 12 0.002907
0.000508 -1.24046 13 0.001687
0.000547 -1.48529 14 0.001202
0.000586 -1.46878 15 0.004948
0.000625 -1.201 16 0.024254
0.000664 -0.73669 17 0.028982
0.000703 -0.48443 18 0.007292
0.000742 -0.94125 19 0.011437
Test bearing with half load

9. Test bearing with no load
0 -0.1139 0 0.004776
3.91E-05 0.271923 1 0.002378
7.81E-05 0.071036 2 0.002604
0.000117 0.238429 3 0.00217
0.000156 0.886815 4 0.00164
0.000195 0.611539 5 0.002626
0.000234 0.139645 6 0.002007
0.000273 0.469521 7 0.002204
0.000312 0.691121 8 0.000881
0.000352 0.551183 9 0.002257
0.000391 0.653564 10 0.00223
0.00043 0.561684 11 0.000715
0.000469 0.253604 12 0.004183
0.000508 0.21962 13 0.005829
0.000547 -0.08225 14 0.001397
0.000586 -0.63509 15 0.006918
0.000625 -0.37527 16 0.035974
0.000664 -0.14344 17 0.038346
0.000703 -0.72063 18 0.018899
0.000742 -0.8904 19 0.016752
Test bearing with no load
Healthy condition
The vibration signals only in the axial directions are presented here for discussion purpose.
Since the amplitude of vibration is observed high in axial direction than in horizontal or in
vertical direction. A typical time domain vibration signals for speed of full load at 731
RPM, half load at 1877 RPM and No load at 1940 RPM

Fig 13.Plot for healthy Bearing at full load condition speed of 1731RPM
Fig 14.Plot for healthy Bearing at half load condition speed of 1877 RPM

Fig 15.Plot for healthy Bearing at no load condition speed of 1940 RPM
Ball Fault
The time domain and frequency domain vibration signals for Ball Fault
are represented in Fig. 16 ,17and Fig. 18
Fig 16.Plot for Ball fault Bearing at full load condition speed of 1730 RPM

Fig 17.Plot for Ball fault Bearing at half load condition speed of 1780 RPM
Fig 18.Plot for Ball fault Bearing at no load condition speed of 1945 RPM

Inner rays fault
The time domain and frequency domain vibration signals for Inner rays Fault
are represented in Fig. 19,20 and Fig.21
Fig 19. Plot for Inner rays fault Bearing at full load condition speed of 744 RPM
Fig 20.Plot for Inner rays fault Bearing at half load condition speed of 744 RPM

Fig 21.Plot for Inner rays fault Bearing at no load condition speed of 1955 RPM

CHAPTER 6
CONCLUSION
A number of experiments have been carried out to investigate the effectiveness condition
monitoring of bearings. A defective bearing which a simulated defect on the inner race, ball
fault was used in conjunction with a healthy bearing at different loading conditions. The
vibration signals obtained from an accelerometer were also measured and analyzed for
comparative purposes.
The time-domain statistical parameters and frequency-domain modified Peak Ratio
were calculated and compared. This study revealed that this technique is demonstrably
superior to vibration acceleration measurements for detecting incipient defects in bearings.

CHAPTER 7
REFERENCES
1. Yong-Han Kim, Andy C C Tana, Joseph Mathew and Bo-Suk Yang
2. Khalid F. Al-Raheem, Asok Roy, K. P. Ramachandran, D. K. Harrison
3. Dong Wang, Qiang Miao
4. Dhirendra Nath Thatoi, Harish Ch. Das Hindawi Publishing Corporation
Advances in Mechanical Engineering Volume 2012
5. Khalid F. Al-Raheem, Waleed Abdulareem International Journal of Mechanic
Systems Engineering (IJMSE) Vol.1 No.1 November 2011
6. Liu ziran, He tao, Jiang guoxing Project supported by the foundation of Henan
university of technology 2013
7. Endo Hiroaki, Sawalhi Nader

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