Defects inside the rolling contact bearings can be evaluated by analysing the vibration signatures from ball bearing housing

1. Detection Of Roller Bearing Defects
By Vibration Analysis
Presenting By:
NASEEL IBNU AZEEZ M.P
Roll No:12
M-Tech MD
AJCE-KANJIRAPALLY
Guided By:
Mr.TOMS PHILIP
Ast. Professor
Mechanical Engineering
AJCE-KANJIRAPALLY

2. BEARINGS
A bearing is a machine element that
constrains relative motion between
moving parts to only in the desired
motion
The term "bearing" is derived from
the verb "to bear; a bearing being a
machine element that allows one part
to bear (i.e., to support) another.

3. Bearing
Sliding Contact Rolling Contact

4. Tapper Bearing Ball Bearing Cylindrical Bearing
Rolling Contact bearing carries a load by placing round elements
between two bearing rings.The relative motion of the pieces causes
the round elements to roll with very little rolling resistance and with
little sliding.
Rolling Contact Bearings

5. Ball Bearing
Ball
Shaft
Outer Ring
Inner Ring

6. Bearing Defects
Causes Of Rolling Bearings Defects
Inappropriate use of bearings
Faulty installation or improper processing
Improper lubricant, lubrication method or
sealing device
Inappropriate speed and operating
temperature
Contamination by foreign matter during
installation
Abnormally heavy load

10. Roller Bearing Defect Detection
Magnetic particle testing
Artificial visual detection
Eddy current testing
Optical detection
Acoustics & Vibration analysis

11. Vibration Analysis
Cracks change the original vibration modal parameters
of roller structure, so whether there are cracks defects
in the roller can be distinguished by impulse response
features.
If there are cracks in the structure, theirs damping
coefficient and stiffness will be changed, which will
reflect on damping ratio and natural frequency.
Damping ratio of the structure is increasing with the
extension of the cracks, while natural frequency is
reducing.

12. Condition Monitoring
Sensor(s) Cables Signal Conditioning
Data Acquisition & Storage Communications
Remote Analysis and Diagnostics

13. What to measure in vibration?
Peak values of:
1. Displacement
2. Velocity
3. Acceleration
Being related to each other, measurement of one leads to
determination of the other two.
𝐷𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 =
𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛
(2𝜋𝑓)2
𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 =
𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛
(2𝜋𝑓)
𝑥 = 𝑥 𝑑𝑡
𝑥 = 𝑥 = 𝑑𝑡𝑑𝑡

14. Signal Processing
Input Sampling
Anti-
aliasing
Filter
A/D
Convertor
Windows
&
Input
Buffer
FFT Averaging
Display
&
Storage
Signal processing is an area of systems engineering, electrical
engineering and applied mathematics that deals with operations on or
analysis of signals, or measurements of time-varying or spatially varying
physical quantities.
Digital Signal Processing(DSP)

15.  Sampling
In signal processing, sampling is the reduction of a continuous
signal to a discrete signal. A sample refers to a value or set of
values at a point in time and/or space.
A sampler is a subsystem or operation that extracts samples from
a continuous signal.
A theoretical ideal sampler produces samples equivalent to the
instantaneous value of the continuous signal at the desired points.
s
𝑆𝑎𝑚𝑝𝑖𝑛𝑔 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦, 𝑓𝑠 =
1
𝑇𝑠
𝑆𝑎𝑚𝑝𝑙𝑒𝑑 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 = 𝑆 𝑛𝑇𝑠 , 𝐹𝑜𝑟 𝑖𝑛𝑡𝑒𝑔𝑒𝑟 valu n

16.  Anti-aliasing Filter
Most sampled signals are not simply stored and reconstructed. But
the fidelity of a theoretical reconstruction is a customary measure of
the effectiveness of sampling. That fidelity is reduced when s(t)
contains frequency components higher than 𝒇 𝒔
𝟐 Hz is known as
aliasing of signal.
Any signal bandlimited to maximum frequency 𝑓𝑚 can be perfectly
reconstructed from its sample if the sample rate, 𝑓𝑠≥ 2𝑓𝑚 (Nyquist
rate)

17.  Analog-to-digital converter
An analog-to-digital converter(ADC, A/D or A to D) is a device
that converts a continuous physical quantity to a digital number that
represents the quantity's amplitude. The result is a sequence of
digital values that have converted a continuous-time and continuous-
amplitude analog signal to a discrete-time and discrete
amplitude digital signal.
Anti-aliasing
filter
ADCx(t)
x [n]
DSP

18.  Signal Windowing
Most digital signals are infinite, or sufficiently large that the dataset
cannot be manipulated as a whole. Sufficiently large signals are also
difficult to analyze statistically, because statistical calculations require
all points to be available for analysis. In order to avoid these
problems, engineers typically analyze small subsets of the total data,
through a process called windowing

19.  Fast Fourier Transform(FFT)
This is a method of taking a real world,time-varying signal and
splitting it into components, each with an amplitude, a phase, and a
frequency. By associating the frequencies with machine
characteristics, and looking at the amplitudes, it is possible to
pinpoint troubles very accurately.
𝑥 = 𝐴1 𝑒−ξ𝜔 𝑛 𝑡 cos 1 − ξ2 𝜔 𝑛 𝑡 + 𝜑1 +
𝑋𝑠 sin(𝜔𝑡 − 𝜑)
[1 − (1 − 𝜔 𝜔 𝑛)
2
]2+[(2𝜉 𝜔 𝜔 𝑛)
2
]

21.  Averaging
Signal averaging is a signal processing technique applied in
the time domain, intended to increase the strength of
a signal relative to noise that is obscuring it.
Consider v(k) is the contaminated signal
Mathematically,
v(k) = vs(k) + vnoise(k),
vs(k) being the desired periodic signal
vnoise(k) the unwanted noise
Signal averaging is performed by accumulating and partitioning v(k),
and adding the partitions with the hope that the noise adds
destructively while the desired signal builds up.

23. Experimental Setup

24. Accelerometer Sensor
Sample Defect Is Shown as Red Spot

25. Outer Race
Inner Race
Bearing Pitch
Diameter (D)
Cage
Bearing Used in Test Motor
(SKF 6306)
No. of Balls = n = 8
Rotational Speed, N = 1480 rpm
Ball diameter, d = 12.3 mm
Bearing pitch dia, D = 50.8 mm
Ball contact angle =00

26. Vibration
Analysis
Time
Domain
Frequency
Domain
Amplitude
Time
Amplitude
Frequency
FFT

27. Time Domain Analysis
Time Domain Zooming

28. Time Domain
Splitting
𝟐 𝐧𝐝 𝐝𝐞𝐫𝐢𝐯𝐚𝐭𝐢𝐯𝐞

30. Frequency Domain Analysis

31. Characteristic Frequencies of
Bearings
𝐹𝐵𝑃𝐹𝑂 =
𝑛 𝑏
2
𝑁
60
1 −
𝑑
𝐷
cos 𝛽 𝐹𝐵𝑆𝐹 =
𝐷
𝑑
𝑁
60
1 −
𝑑
𝐷
2
cos 𝛽
𝐹𝐵𝑃𝐹𝐼 =
𝑛 𝑏
2
1 +
𝑑
𝐷
cos 𝛽 𝐹𝐹𝑇𝐹 =
1
2
𝑁
60
1 −
𝐷
𝑑
cos 𝛽
𝐹𝐵𝑃𝐹𝑂 = Ball passing frequency outer race
𝐹𝐵𝑃𝐹𝐼 = Ball passing frequency inner race
𝐹𝐵𝑆𝐹 = Ball spin frequency
𝐹𝐹𝑇𝐹 = Fundamental train frequency
n = no. of balls
N = rotational speed in rpm
d=ball diameter
D=bearing pitch diameter
𝛽=ball contact angle with
the races

32. Characteristic Frequencies For SKF-6306
𝐹𝐵𝑃𝐹𝑂=74.77 Hz
𝐹𝐵𝑃𝐹𝐼=122.55 Hz
𝐹𝐵𝑆𝐹=47.95 Hz
𝐹𝐹𝑇𝐹=9.3 Hz
No. of Balls,n=8
Rotational Speed,N = 1480rpm
Ball diameter,d =12.3 mm
Bearing pitch dia,D=50.8 mm
Ball contact angle=00

34. Positioning Of Bearing Defect

36. Conclusions
 Trend of overall frequencies and vibration spectrum provide useful
information to analyze defects in roller bearings.
 This technique can provide early information about progressing
malfunctions.As a result, the necessary control action can be taken
on the machine in advance.
 The distinct and different behavior of vibration signals from
bearings with inner race defect and outer race defect helps in
identifying the defects in roller bearings.

37. Reference
 Prediction of Defects in Roller Bearings Using Vibration Signal Analysis
H. Mohamadi Monavar, H. Ahmadi and S.S. Mohtasebi
World Applied Sciences Journal 4 (1): 150-154, 2008 ISSN18184952,IDOSI
Publications,2008
 Vibration Analysis using Time Domain Methods for the Detection of small Roller
Bearing Defects
Tahsin Doguer,Jens Strackeljan
SIRM 2009 - 8th International Conference on Vibrations in Rotating
Machines,Vienna, Austria, 23 - 25 February 2009
 Prediction of Defects in Antifriction Bearings using Vibration Signal Analysis
M Amarnath,R Shrinidhi,A Ramachandra,S B Kandagal
IE(I) Journal-MC
 Monitoring and Analysis of Vibration Signal Based On Virtual Instrumentation
Sunita Mohanta1, Umesh Chandra Pati
International Journal of Advanced Computer Research
ISSN:2249-7277 ISSN:2277-7970) Volume-3 Number-1 Issue-8 March-2013

38. Reference Cont.
 Experimental Study on Condition Monitoring of Low Speed Bearings : Time Domain
Analysis
Eric Y. Kim,Andy C. C. Tan,Bo-Suk Yang and Vladis Kosse
5th Australasian Congress on Applied Mechanics, ACAM 2007 10-12
December 2007, Brisbane, Australia
Text Books
 Mechanical Vibrations:Theory and Practice
Author:Shrikant Bhave
Publisher:Pearson
 Mechanical Vibrations
Author:V P Singh
 Advanced Engineering Mathematics
Author:Erwin Caryzig
Publisher:Willey
 Digital Signal Processing
Author:A Anand Kumar
Publisher:PHI Learning

39. THANK YOU