Rolling Element Bearing Fault Detection with a Single Point Defect on.Pdf

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The 11th Asia Pacific Indust ria l Engineering a nd Ma na g ement Syst ems Conference

The 14th Asia Pacific Regional Meet ing of International Foundat ion for Product ion Research

Melaka, 7 – 10 December 2010

Rolling Element Bearing Fault Detection with a Single Point Defect on

the Outer Raceway Using Finite Element Analysis

Prof. Dr. Zahari Taha1, Nguyen Trung Dung

2,*

1 Faculty of Manufacturing Engineering and Technology Management, Universiti Malaysia Pahang, 26300 Kuantan, Pahang,

Malaysia

[email protected] of Mechanical Engineering, Ho Chi Minh City University of Technology, 70000 Ho Chi Minh City, Vietnam

[email protected]/ [email protected]

Abstract - In this paper, a method based on finite element analysis is presented for defect detection in rolling

element bearing with a single point defect on outer raceway of the bearing using vibration analysis in the

frequency domain. A load mechanism is also proposed to simulate the nodes excitation force as external loading.

Finite element models of housing and outer raceway of the bearings are created and vibration response

analyzed using commercial finite element software ABAQUS. The identification of bearing defects is obtained

by extracting characteristic defect frequency from the vibration signal of the defective bearing. Finally,

experiments are conducted to validate the simulation results.

Keywords: frequency domain, vibration analysis, bearing defect detection, finite element analysis

1. INTRODUCTION

Among mechanical components, researchers pay greatattention to rolling element bearings because of their

significant industrial importance. Rolling element bearings are

frequently used in rotating machinery due to their carrying

capacity and low-friction characteristics. Proper functioning of

these appliances depends on the smooth and quiet running of

the bearings. In industrial applications, these bearings are

considered as critical mechanical components and a defect in

such a bearing causes malfunction and may even lead to

catastrophic failure of the machinery. Defects in bearings may

arise during use or during the manufacturing process such as

crack damage, spalling, corrosion, fatigue failure, etc.

Therefore detection of these defects is important for condition

monitoring as well as quality inspection of bearings. There are

different methods used for detection and diagnosis of bearing

defects which are classified as vibration and acoustic

measurements, temperature measurements and wear debris

analysis. Among these, vibration analysis is the most common

method used in monitoring applications since a local defect

produces successive impulses at every contact of defect andthe rolling element, and the housing structure is forced to

vibrate at its natural modes.

2. LITERATURE REVIEW

Two approaches have been adopted by researchers for

creating localized defects on bearings to study their vibration

response. One is to run the bearing until failure and monitor

the changes in their vibration response. Usually the failure is

accelerated by either over-loading, over-speeding or using lack

of lubricants of the bearings. The other one is to intentionally

create defects in the bearings by different techniques such as

acid etching, spark erosion, electrical discharge machining

(EDM), scratching or mechanical indentation, and then

measure their vibration response and compare it with that of

good bearings. The former approach of life tests is quite

time-consuming. On the other hand, the testing of bearings

with simulated defects is much quicker but preparation of the

defective bearings requires special techniques. Several






techniques have been applied to measure and analyze the

vibration response of bearings with localized defects. These

techniques are not totally independent but support one another

in some cases.

A. Frequency-Domain Approach

Frequency-domain or spectral analysis of the vibration

signal is perhaps the most widely used approach of bearing

defect detection [1]. The interaction of defects in rolling

element bearings produces pulses of very short duration

whenever the defect strikes or is struck because of the

rotational motion of the system. These pulses excite the

natural frequencies of bearing elements and housing structures

and result in an increase in the vibrational energy at these high

frequencies. Therefore, monitoring the increase in the level of vibrations in the high-frequency range of the spectrum is an

effective method of predicting the condition of rolling element

bearings.

Each bearing element has a characteristic rotational

frequency. These characteristic defect frequencies can be

calculated from kinematic considerations which are the

geometry of the bearing and its rotational speed. Assuming no

skidding of rolling elements, the outer raceway defect

frequency is given by the following expressions for a bearing

with a stationary outer raceway [2].

The rotational speed of the cage (cage frequency)c

f is:

1 cos2

s bc

m

f d f

d D

§ · ¨ ¸

© ¹(1)

Where f s is the rotational speed of the inner ring, d m is the

pitch diameter of bearing, d b is the rolling element diameter,

and α is the contact angle.

The rate at which balls pass a point in the groove of theouter raceway (also called the ball-pass-outer-raceway

frequency or outer raceway defect frequency) is:

1 cos2

s bbpor c

m

Zf d f Zf

d D

§ · ¨ ¸

© ¹(2)

where Z is the number of balls or rollers.

In some studies, it has been mentioned that ‘noise’ or

vibration from other sources is presented in the vibration

signal from the bearing. Therefore, in order to improve the

signal-to-noise ratio and make the spectral analysis more

effective, some signal processing techniques have been

reported. One of the most popular ones is envelope analysis or

the high-frequency resonance technique (HFRT) which helps

in the identification of bearing defects by extracting

characteristic defect frequencies from the vibration signal of

the defective bearing. A review of this technique has been

presented by [3]. Each time a defect strikes its mating element,

a pulse of short duration is generated that excites the

resonances periodically at the characteristic frequency related

to the defect location. By demodulating one of these

resonances, a signal indicative of the bearing condition can be

recovered. The process of extraction of demodulated spectra by the HFRT is shown in Fig. 2.1.

Figure 2.1 The process of extraction of demodulated

spectra by HFRT [4].

B. Load Distribution

The loads carried by the ball and roller bearings aretransmitted through the rolling elements from one ring to the

other. As mentioned in [2], the relationship between load and

deflection is:

nQ K G (3)

in which n = 1.5 for ball bearings and n = 1.11 for roller

bearings, Q is the ball or roller normal load, G is

deformation, and K is load-deflection factor.






For a rigidly supported bearing subjected to radial load,

the radial deflection at any rolling element angular position is

given by:

1

cos 2r d P \ G G \ (4)

in whichr G is the ring radial shift, occurring at 0\ and

P d is the diametrical clearance.

Figure 2.2 Rolling element load distribution for different

amounts of clearance [2].

Eq. (6) may be rearranged in terms of maximum deformation

as follows:

max

11 1 cos

2\ G G \

H

ª º « »¬ ¼

(5)

where ε is the load distribution factor given by:

11

2 2

d

r

P H

G

§ · ¨ ¸© ¹

(6)

Thus, for ball bearings under pure radial load and zero

clearance, we have:

max

4.37

cos

r F Q

Z D (7)

3. DEVELOPMENT OF MODEL

Figure 3.1 SFK 1206 ETN9 bearing geometry.

Figure 3.2 Bearing outer raceway structure after partition

into 64 parts.

To monitor the condition of a bearing, a SKF 1206 ETN9

self-aligning ball bearing is used in this study. Fig. 3.1 shows

the geometry and dimensions of bearing. The finite element

method is adopted to observe the dynamic response of the

structure. Fig. 3.2 is the outer raceway structure of the bearing

which is partitioned into 64 parts.

48.5

185

2 2

5 0

8 9

3139

92.5

52

x

y

Clamped surfaces

Figure 3.3 Bearing housing structure.

Also, the finite element model of the housing is created

and shown in Fig. 3.3. The housing and outer raceway

structures are discretised into 71846 and 14091 4-node linear

tetrahedron elements, respectively. The material of housing

structure and outer raceway is isotropic steel with the

properties as, E = 209 GPa, µ = 0.3, ρ = 7800 kg/m3.






Figure 3.4 Finite Element Model of Assembly Structure

Fig. 3.4 shows the finite element models of both housing and

outer raceway after meshing. In order to simulate the dynamic

loading model for finite element vibration analysis, the radialload acting on each node along the inner circumference of the

loading zone of the outer raceway have to be developed. It is

assumed that there is no diametrical clearance in the bearing,

hence the loading zone would be -900 < ψ < 90

0. According to

[5], the optimum number of elements along the circumference

of the bearing’s outer raceway and the result is 64 elements.

Figure 3.5 Radial load acting on one node.

r F

Node No. 16

Node No. 17

Node No. 18

Defect position

Figure 3.6 Radial load model at initial position.

Fig. 3.5 and 3.6 show the radial load acting on one node

and the model at the initial position, respectively. The radial

loading models are created at 33 nodes in the loading zone. If

a ball passes a defect, the excitation force will be dramatically

amplified by a factor of 6 as proposed in [6]. The shaft

rotation speed is assumed as f s = 1000 RPM = 16.67 Hz . Thus,

the rotational speed of the cage (cage frequency) is calculated

as:

1 cos 411.1112

6.852

s bc

m

f d f RPM

d

Hz

D § ·

¨ ¸© ¹

The outer raceway defect frequency is:

14 6.852 95.928bpor c f Zf Hz u

If there is a peak at 95.928bpor f Hz , it can be

concluded that there is a defect on the outer raceway of the

bearing.

4. EXPERIMENTAL SET-UP

A test rig is designed to conduct the experiments for

bearing defect detection. Fig. 4.1 below shows the

experimental diagram.

1 2 3 4 5 6

78

1. Lathe machine spindle

2. Shaft

3. Accelerometer sensor

4. Bearing housing

5. Bearing

6. Load

1t

2t

3t

4t

5t

6t

1Q 2Q

Point P






7. Data Acquisition (DAQ)

8. Computer

Figure 4.1 Experiment set-up diagram.

Figure 4.2 The shaft, bearing housing, load and the

accelerometer on the lathe machine.

Figure 4.3 The vibration module, DAQ and computer.

The vibration module and DAQ used are NI 9233 and NI

cDAQ-9172. The software used to obtain the data and analyze

it in NI SignalExpress 1.0. A defect is created intentionally on

the outer raceway of the bearing with the size of 2 mm in

diameter and depth using Electrical Discharge Machining

(EDM).

Figure 4.4 The artificial defect on the outer raceway (the

defect area is circled).

5. RESULTS AND DISCUSSION

Figure 5.1 Simulation acceleration responses at point P in

time domain.

Table 1 Time parameters of acceleration response of outer

raceway defected bearing

Type of

bearingRMS

Peak Level Peak to

Peak Max Min

Healthy 1143.44 3565.08 -3583.62 7148.7

Defected 1664.73 6022.4 -5847.92 11870.3

Difference (%) 45.59% 68.93% 63.18% 66.05%


frequency domain.

s f bpor f Defected

Defected

Time (s)

a (

m m / s 2 )

Healthy

Time (s)

a ( m m / s 2 )







frequency domain (cont.).

Figure 5.3 Zoomed simulation acceleration responses at point

P in frequency domain.

Figure 5.4 Experimental acceleration responses at point P in

frequency domain.

Figure 5.5 Zoomed experimental acceleration response at

point P in frequency domain.

s f

s f

bpor f

2 s f s f

s f 2 s f

bpor f

2 s f s f

bpor f

Healthy

Defected

Defected

Healthy

Defected

Healthy

s f

2 s f s f

Healthy






Fig. 5.1 – 5.5 and Table 1 show the comparison of

vibration responses between the outer raceway defected

bearing and the healthy bearing model in time and frequency

domains, respectively. It is observed that there is a big

difference between defected and healthy bearing vibration

signals in time domain and a peak at the outer raceway defect

frequency f bor = 95.928 Hz . Therefore, it can be concluded that

there is a defect on the outer raceway of the bearing.

It is observed that in both simulation and experiment, the

defect on the bearing can be detected. However, the squared

magnitudes are different and the one in simulation is slightly

larger (2E+06 in simulation compare with 1.17E+06 in

experiment as shown in Fig. 5.6). This is because the finite

element model used is simplified. Also, the outer racewaymodel is partitioned into 64 parts and the dynamic loads are

developed on 33 nodes in the load zones only. Moreover, the

amplification factor of 6 (for the excited force when the

impact occurs) is probably pretty big.

6. CONCLUSION

In this study, the finite element model of a bearing and

housing structure has been developed using the commercial

finite element method software ABAQUS. Also, a dynamic

loading distribution is developed to simulate the load

distribution on the outer raceway due to load transfer from the

shaft through the balls. Moreover, the impulse force due to

impact between the defect and the bearing components is

proposed by amplifying the excitation force on the defected

area with a factor of 6. Then the models are analyzed to obtain

the vibration signal in the frequency domain and the vibration

signal of defected bearing is compared with the one of healthy

bearing for defect detection. Experiments were conducted to

validate the simulation results of the software. The simulation

and experiment are found to be in good agreement.Vibration analysis is a powerful method for bearing defect

detection. With this method, it is possible to predict the

condition of a bearing.

ACKNOWLEDGEMENT

I would like to give my sincere gratitude and

appreciation to my supervisor, Prof. Zahari Taha, who has

been an excellent adviser with continuous encouragement and

guidance in completing the research. Also, I would like to give

my thanks to ASEAN University Network / Southeast Asia

Engineering Education Development Network

(AUN/SEED-Net) and JICA for supporting me with their

financial support in this research. Special thanks to Mr. Amir

Radzi and Mr. Norhafidzan for their valuable advices and

supports throughout the research. Many thank to the CPDM

members, the staff of Engineering Design and Manufacture

Department, Engineering Faculty, University of Malaya for

their supports, kindness, and helpfulness. An immeasurable

debt of gratitude also goes to my colleagues for their advices

and helping me on the difficult moments.

REFERENCES

[1] N. Tandon, A. Choudhury, A review of vibration and

acoustic measurement methods for the detection of defects inrolling element bearings, Tribology International 32 (1999),

469 – 480.

[2] Tedric A. Harris , Rolling bearing analysis, Fourth

edition, A Wiley-Interscience Publication, JOHN WILEY &

SONS, INC, 2001.

[3] P.D. McFadden and J.D. Smith, Vibration monitoring

of rolling element bearings by the high-frequency resonance

technique – a review, Tribology International 17 (1984) 3 – 10.

[4] N. Tandon, A. Choudhury, A review of vibration and

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[5] Kadarno Purwo, Vibration Analysis of Defected Ball

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[6] Zeki Kıral, Hira Karagulle, Simulation and analysis of

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defects, Tribology International 36 (2003) 667 – 678.[7] Wei He, Zhi-Nong Jiang, Kun Feng, Bearing fault

detection based on optimal wavelet filter and sparse code

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[8] P.D.McFadden, J.D. Smith, Model for the vibration

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[9] Singiresu S. Rao, Mechanical Vibrations, Second

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[10] Zeki Kıral, Hira Karagulle, Vibration analysis of

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Rolling Element Bearing Fault Detection with a Single Point Defect on.Pdf