Kiral 2003 Tribology-International

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Tribology International 36 (2003) 667678

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Simulation and analysis of vibration signals generated by rollingelement bearing with defects

Zeki Kiral, Hira Karagulle

Department of Mechanical Engineering, Dokuz Eylul University, Engineering Faculty, 35100 Izmir, Turkey

Received 17 June 2002; received in revised form 30 December 2002; accepted 13 January 2003

Abstract

Dynamic loading of a rolling element bearing structure is modeled by a computer program developed in Visual Basic programminglanguage. The vibration response of the structure to the dynamic loading is obtained using a standard finite element package I-DEAS. A force model is proposed to model the localized rolling element bearing defects. Time and frequency domain analysesare performed for diagnostics of rolling element bearing structures. Statistical properties of the vibration signals for healthy anddefected structures are compared. The envelope (HFRT) method is employed in the frequency domain analysis. The effect of therotational speed on the diagnostics of rolling element bearing defects is investigated. An optimum sensor location on the structureis sought. Effect of the structure geometry on the monitoring techniques is studied. An optimum monitoring method can be employedby analyzing the rolling element bearing structure following the procedure proposed in this study. The present commercial computeraided engineering packages can be used in special engineering applications such as condition monitoring of rolling element bearings.

2003 Elsevier Science Ltd. All rights reserved.

Keywords: Rolling element bearing; Condition monitoring; Finite element vibration analysis

1. Introduction

Rolling element bearings are a common component inmachinery. Therefore they have received great attentionin the field of condition monitoring. Rolling elementbearings are manufactured by assembling differentcomponents: The rolling elements, the outer ring and theinner ring, which are in contact under heavy dynamicloads and relatively high speeds. The Hertzian contactstresses between the rolling elements and the rings areone of the basic mechanisms that initiates a localizeddefect. When a rolling element strikes a localized defectan impulse occurs and this excites the resonances of thestructure. The vibration signature of a damaged bearingconsists of exponentially decaying ringing. Theseimpulses will occur with a period determined by thelocation of the defect, the geometry of the bearing andthe type of the bearing load.

Corresponding author. Tel.: +90-232-343-6600; fax: +90-232-388-7864.

E-mail addresses: [email protected] (Z. Kiral);

[email protected] (H. Karagulle).

0301-679X/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved.doi:10.1016/S0301-679X(03)00010-0

White [1] describes a method for simulating themachinery fault signals which are impulsive in natureand analyzed them. The basic understandings of the rol-ling element bearing vibrations for a defected case anda well-established model that considers the load distri-bution around the circumference of the rolling elementbearing and the impulse response of the bearing structureare proposed in Ref. [2]. The vibration model given inRef. [2] is extended to include multi point defects atarbitrary locations in Ref. [3]. Another theoretical modelwhich includes the natural modes of the rings is pro-posed in Refs. [4,5]. The mode summation method isemployed to find the vibratory response of the bearingsubjected to radial or axial load for the cases of differentdefect locations. The bearing vibration signals are mod-eled as a combination of different sources such as fault,modulation due to non-uniform loading, flexural bearingmodes, machinery induced vibrations and noise in Ref.[6].

There are several techniques for condition monitoringof rolling element bearings. Among them vibration andacoustic measurements are most widely used. The readercan find a detailed review for these methods in Ref. [7].


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668 Z. Kiral, H. Karagulle / Tribology International 36 (2003) 667678

The vibration measurement methods can be classified asin time and in frequency domains. A brief review on the

monitoring techniques in time and in frequency domain

can be found in Ref. [8]. Honarvar and Martin [9,10]

use the third and fourth moment of the vibration signalsknown as skewness and kurtosis, respectively, for bear-

ing failure detection. McFadden and Smith [11] presentthe basic understanding of the high-frequency resonance

technique. Lou et al. [12] propose a method based on

extracting the dynamic model of the bearing system fromthe experimental vibration signals to design a proper

fault detection filter.Recently timefrequency domain analysis has become

popular. The wavelet method is used by the researchers

[1317] in condition monitoring of rolling element bear-ings due to its superiority in time and frequency resol-ution while processing the vibration signals. Holm-

Hansen and Gao [18,19] propose the sensor integrated

vibration analysis technique and developed an analytical

and finite element model to predict the changes in thebearing dynamic loading.

The main sources of acoustic emission are plastic

deformation or growth of cracks in metals. The acoustic

emission technique is based on sensing the acoustic

waves having a frequency greater than 100 kHz and hasbeen used in condition monitoring of rolling element

bearings [20]. Developments in the artificial intelligencetechnology have brought up a new monitoring technique

by using neural networks. In this method, the statistical

properties of the vibration signals extracted from either

time or frequency domain are entered into neural net-

works for training purposes and then the trained net-works distinguish the defective bearing from the normalbearing [21,22].

In this study, dynamic loading models for healthy and

defected rolling element bearing structures are

developed and the vibration response of the bearing

structure is performed using the finite element method.A new force model is proposed to simulate the force

variation and impact formation when the rolling

elements roll over a local defect. The success of the dif-

ferent statistical properties such as kurtosis, crest factorand RMS values of the simulated vibration signals is

investigated for condition monitoring purpose. The

envelope technique is employed to identify the bearing

characteristic defect frequencies. The effect of the bear-

ing structure geometry on the selection of the signal pro-

cessing technique is investigated.

2. Model for dynamic loading

Rolling element bearings are subjected to moving dis-tributed radial loads. A proper loading model including

the bearing kinematics and load distribution should be

employed for analyzing the dynamic response of the

bearing structure. The studies on condition monitoring

are focused on finding the best signal processing tech-nique to detect an incipient failure. The simulated or

experimental vibration signals are used in these studies.

The experimental signals carry the information about thebearing structure which is the transmission path of the

vibration, but the artificial signals suffer from containingstructural information. The proposed method overcomes

this deficiency and makes it possible to use bearingvibration signals containing structural information.

The bearing structure for which the dynamic analysis

is performed is shown in Fig. 1. The structure houses a

6205 model single row deep groove ball bearing. The

outer ring of the ball bearing is modeled within the hous-

ing structure. The dimensions of the ball bearing are

given in Fig. 2.The solid model of the housing structure is discretized

into 23,964 finite elements and the resulting node num-ber is 37,894. The element type used in the discretization

is a 10-node parabolic tetrahedron having 3 degrees of

freedom at each node. The bearing load is assumed to

be an unbalanced force. Therefore, the radial load moves

around the circumference of the outer ring as the shaft

rotates. The single radial load transforms to a distributed

load because the inner ring is in contact with more thanone ball during the rotation. The load distribution is

shown in Fig. 3.

The form of the radially distributed load is given in

Ref. [2] as,

q(f) (1)

q0 11

2e(1cosf)

n

0

for fz f f

z

elsewhere

Fig. 1. Rolling element bearing structure.


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669Z. Kiral, H. Karagulle / Tribology International 36 (2003) 667678

Fig. 2. Dimensions of the ball bearing.

Fig. 3. Load distribution in a rolling element bearing.

where e is the load distribution factor and given in Ref.[5] as e = 0.5(1 - Cd/ 2dmax), Cd is the diametral clear-ance, dmax is the maximum deflection in the direction of

the radial load. In ball bearings e has the value between0 and 0.5. q0 is the maximum load intensity at f = 0,and can be approximated by q0 = 5Fr/Z cos a, whereFr is the radial load. The rolling elements transfer the

radial load to the outer ring during their rotation with

cage frequency expressed as fc = fs/ 2(1db/dm cos a)where fs is the shaft frequency.

In general, the bearing loads can be classified as pointand circumferential. In the point loading case, the direc-

tion of the radial load does not change and the nodes in

the load zone are loaded with a constant amplitude loadwith the frequency Zfc. The nodes outside the load zone

do not carry load. The loading mechanism for the cir-

cumferential loading is more complicated than the point

loading case. In this case the radial load rotates also with

shaft and therefore every node is subjected to load when-

ever a ball carrying the load moves over on that node.

The load amplitude is not constant and the loading fre-

quency of a node has two components composed of Zfc,

and the combination of fs and fc. The form and the para-

meters of bearing loading due to an unbalance are shown

in Fig. 4.

The number of the node in the load zone Nz = 2N1. The number N is determined by the nodes that satisfy

the condition |qniq|fz. In Fig. 4, q represents the shaft

Fig. 4. The form and parameters of radial loading.

rotation, qni (i

=1Nz) stands for the angular position ofthe nodes in the load zone and qbj (j = 1Z) stands for

the angular position of the balls. Loading functions are

defined versus time, taking all the variables into accountby using a computer code developed in Visual Basic [23]programming language. A sample loading function is

seen in Fig. 5 for the node at 90.The dynamic load model is formed using 80 nodes

lying on the circumferential path. The number of exci-

Fig. 5. x components of the radial load acting on the node at

225,1000 rpm.


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tation functions is 160. The vibration analysis of the

bearing structure is performed using a standard finiteelement package I-DEAS under the action of these exci-

tation functions simultaneously. The I-DEAS package

uses the mode summation technique and 10 modes areused to calculate the dynamic response of the bearing

structure. The time increment is chosen as t = 1 /20f10 in order to get the contribution of all the modes in

the considered region. A sample view for the dynamic

response at point P3 at 1000 rpm is seen in Fig. 6.The dynamic response of the structure includes low

and high frequency components as shown in Fig. 6. Low

frequency components are directly related to the shaft

rotation and high frequency components can be attri-

buted to the contribution of the natural modes.

3. Defect modeling

The mechanism of defect formation can be different

in a rolling element bearing. Sudden changes in the rad-

ial load or Hertzian contact stresses may result in a local

defect during operation. An impulse like force is pro-

duced whenever a rolling element in the load zone

moves over a defect and the bearing structure is sub-jected to consecutive impulses. The magnitude and the

duration of the impulse force are related with the radial

load carried by the rolling element, defect dimensions,

the velocity of the rolling elements and material proper-

ties. Dynamic stress analysis finds that the maximumstress occurs at about 225 in the bearing structure under

the prescribed loading. A local defect is modeled byamplifying the magnitudes of the radial forces definedfor the nodes which are in the defected area. As stated

above, the value of the multiplier is determined by vari-

ous parameters and this is beyond the scope of this

paper. The amplification constant is chosen simply as 6in this study. The width of the local defect is chosen as

the width between two neighboring nodes. Therefore

force amplification is applied for two nodes which formthe leading and the trailing edges of the local defect

shown in Fig. 7.The loading model for healthy and defected case are

shown in Figs. 8a and b, respectively. The increase in

the radial load amplitude due to impact can be seen in

Fig. 8b. The developed computer code is capable of

Fig. 6. x-displacement response at point P3 at 1000 rpm.

locating the given number of defects at the given pos-

itions and creates the dynamic excitation forces in ASCII

file format suitable for execution in I-DEAS. Durationof the impacts is determined by the rotational speed of

the shaft. Impact duration reduces as the shaft speedincreases. Forced vibration analysis is performed under

the predefined nodal excitation functions including theimpact modeling due to the defect(s). The dynamic

response of the bearing structure at point P3 with defect

is given in Fig. 9.It is observed from Fig. 9 that the dynamic response

of the bearing structure is impulsive in nature because

of the way in which it is generated. The experimental

results presented in Refs. [10,13,17] confirm that thedefected vibration signals have spiky characteristics and

the proposed method can be used to produce vibrationdata for condition monitoring applications.

4. Time domain analysis

In this section, the vibration data are analyzed and

different parameters such as root mean square (RMS),

crest factor (Cf) and kurtosis are assessed with regard to

their effectiveness in the detection of bearing condition.

The defect is located on the outer ring between the nodes

at 225 and 229.5. The vibration data are calculated forpoints P1, P2 and P3 for a broad range of rotational

speed ranging from 1000 to 15,000 rpm. The time

domain properties for a discrete vibration signal x havingNs samples can be expressed as follows

RMS 1NsNs

i 1

x2i, Cf

max(x)min(x)

RMS,

kurtosis

Ns

i 1

(ximean(x))4

NsRMS4

(2)

The displacement response of the bearing structure is

shown in Fig. 10. It can be seen from Fig. 10a that the

RMS value of the displacement for point P2 is an effec-tive parameter in a broad range of shaft speed except

9000, 13,000 and 14,000 rpm. The displacement RMS

values may be used to identify the defect for points P1

and P3 in the speed region from 9000 to 11,000 rpm.

The comparison of the crest factor value shows that this

parameter can be used only for point P2 as a defect indi-cator. The crest factor parameter is not suitable during

defect detection for other two receiving points. The para-

meter kurtosis can be used for point P2 in the low speed

region. But it can be concluded that the ratio between

the time domain parameters of the defected and healthybearings is very small for all speeds and it is difficult todetect a defect by using the displacement results. The

ratios between the statistical parameters of the velocity


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Fig. 7. Impact mechanism.

Fig. 8. Loading model for (a) healthy, (b) defected bearings.

Fig. 9. Dynamic responses of the bearing structure at point P3: (a)

displacement; (b) velocity; (c) acceleration.

responses calculated for healthy and defected bearings

are shown in Fig. 11. The RMS ratios are greater than

those given for the displacements. The point P2 seems

to be a better receiving point until 6000 rpm and points

P1 and P3 give better results at 9000 and 11,000 rpm.The crest factors ratios are given in Fig. 11b and it can

be concluded that point P2 may be used as a fault indi-

cator until 3000 rpm. The crest factor ratios obtained

from the vibration responses fail while detecting a

localized defect at higher speeds. The same observation

is valid for kurtosis ratios between velocities. The kur-

tosis calculated from the readings for point P2 can be a

good fault indicator at low speeds. Different signal pro-

cessing techniques should be applied to extract useful

information at high speeds. The acceleration response of

the structure is frequently used in the monitoring appli-

cations. The ratios of the statistical properties for the

acceleration responses are shown in Fig. 12. The RMS

value of the accelerations give better result at point P2

and at relatively low speeds. The same observation is

valid for the crest factor and kurtosis ratios. It can be

concluded from Figs. 11 and 13 that point P2 seems to

be a better receiving point for defect detection at low

speeds. The vibration signal loses its spiky characteristic

due to the decreasing duration of the impacts and there-

fore invalidity of the time domain parameters at high

speeds is meaningful. The difficulty in detecting the faultat high speeds can be overcome by filtering the vibrationsignal at some frequency bands. The statistical para-


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Fig. 10. Statistical parameters for the displacement response: (a)

RMS; (b) crest factor; (c) kurtosis.

meters calculated for point P2 are shown in Fig. 13 after

band pass filtering. The velocity signals are band passfiltered by using a fourth order Butterworth filter in theMATLAB [24] program. The ratio for RMS and kurtosis

values increases filtering the velocity signal in the 25005000 Hz frequency band and the ratio for crest factorincreases by using 02500 Hz frequency band. Theincrease in the ratios shown in Fig. 14 reflects the benefitof the filtering. The maximum increase occurs in the kur-tosis values and the kurtosis parameter becomes a useful

defect indicator for high speeds by selecting the appro-priate filter band. Band-pass filtering also increases theRMS ratio and brings it to a useful value to distinguish

the defected and healthy bearings.

Fig. 11. Statistical parameters for the velocity response: (a) RMS; (b)

crest factor; (c) kurtosis.

5. Frequency domain analysis

Another conventional approach is processing the

vibration signals in the frequency domain. The basic

indicator is the characteristic defect frequencies in thefrequency domain analysis. The characteristic defect fre-

quencies depend on the rotational speed and the location

of the defect in a bearing. The existence of one of the

defect frequencies in the direct or processed frequency

spectrum is the powerful sign of the fault. The signature

of the defected bearing is spread across a wide frequencyband and can be easily masked with low frequency

machinery vibrations and noise. The consecutive impacts

between the defect and rolling elements excites the res-

onances of the structure and the resonant frequencies

dominate the frequency spectrum. Therefore, the charac-teristic defect frequencies cannot be easily noticed

because of their low amplitudes with respect to res-

onant amplitudes.


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Fig. 12. Statistical parameters for the acceleration response: (a)

RMS; (b) crest factor; (c) kurtosis.

Fig. 13. Statistical parameters of filtered velocity signals for point

P2, at 12,000 rpm.

Different methods are used to amplify the amplitudesat the characteristic defect frequencies. The envelope

method is one of them and frequently used in fault detec-

tion applications. The envelope method can be summar-ized as digitizing the time signal and band-pass filteringit in a region, typically around the resonances of the

structure due to their high signal-to-noise ratio. The dif-

Fig. 14. Increase in the statistical parameters after filtering, at 12,000

rpm.

ficulty in industrial applications is that the resonances ofthe whole bearing structure cannot be easily predicted

and consequently selection of the band-pass filter limitsbecomes difficult. Fig. 15 summarizes the enveloping

procedure. The velocity signal shown in Fig. 15 isobtained for point P2 at 2000 rpm. As can be seen from

the figure, the resonant ringings due to the impacts domi-nate the velocity signal. The Fast Fourier Transform of

the original signal explains this situation clearly. The

outer ring defect frequency does not appear in the fre-

quency spectrum. Filtering the velocity signal around the

resonant frequency makes the signal fully composed of

resonant ringings. The envelope of the filtered signal isobtained taking the Hilbert transform in MATLAB. The

FFT of the enveloped signal includes the outer ring

defect frequency which is defined as,

for Z fs

2db1

db

dmcosa (3)

The existence of the characteristic defect frequencies in

the frequency spectrum of the enveloped signal indicates

a defect. The procedure explained in Fig. 15 can be auto-

matized for field applications. The resonance frequenciesof the bearing structure can be calculated using the finiteelement approach and the vibration signal is filteredaround the desired resonance. In this analysis, the velo-

city and acceleration signals for point P2 are filteredaround 7340 Hz for which the modal displacements are

purely in the y direction. The bandwidth of the band-

pass filter is selected as 4for. Envelope energy is calcu-lated from the FFT of the enveloped signal for the fre-

quency interval (f0.1for) f (f + 0.1for). The band

energy ratios for velocity and acceleration signals at dif-ferent shaft speeds are shown in Fig. 16.

It can be seen from Fig. 16 that the band energy ofthe envelope spectrum can be used as a powerful tool at

low speeds. This is valid for high speeds except some

rotational speeds. The weakness of the method at somehigh rotational speeds may be overcome by employing

different bandwidths or even starting the enveloping

sequence around different resonant frequencies.


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Fig. 15. Enveloping sequence.

Fig. 16. Band energy ratio.

6. Effect of the structure

The source of the vibration in a bearing structure is theinteraction between the elements and the transmission

mechanism of the radial load. Even a perfect bearingproduces vibration because the rolling elements in the

load zone change as the shaft rotates. The contribution

of a local defect can be observed both in time domainand in frequency domain as stated in the previous sec-

tion. Vibration measurements are done generally by

mounting an accelerometer on the suitable location of

the housing structure or in some cases by hand probes.In both cases, the geometry of the housing structure

determines the vibration transmission path and the trans-

mission path changes as the geometry of the housingstructure changes. In the previous studies, the housing

structure is assumed to be either rigid or modeled by

using a springmass model. But the dynamic character-istics of the housing structure which form the vibration

transmission path between the source and the receiver

(sensor) affect the vibration signal and consequently theperformance of the signal processing method employed

in the defect detection. The effect of the structure on thesuccess of the signal processing method is investigated

by using different bearing structure, as shown in Fig. 17.

The finite element model of the new structure consistof 32,426 elements and 52,227 nodes and the material

properties are used as in the first model. The secondstructure is designated to give the first natural frequencyratio as fn1(first)/fn1(second)2.

The statistical properties are calculated for velocity

signal and they indicate that the RMS ratios for pointsP1 and P3 can be used in defect detection at low speeds

in contrast with the results obtained for first structure asseen in Fig. 18a. The RMS ratios are high for point P2

at high speeds and can be preferable in this region. The

crest factor ratios for point P2 can be used only at spe-

cific speeds, but at high speeds, the crest factor fails todetect a defect for every receiving point as shown in Fig.


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Fig. 17. The new bearing structure and its finite element model.

Fig. 18. The ratio of the statistical properties for velocity signal at

point P2: (a) RMS; (b) crest factor; (c) kurtosis.

18b. Kurtosis ratios are smaller than those given for firststructure and may be used for point P2 at 5000 rpm as

seen from Fig. 18c. The kurtosis ratio is not a suitable

indicator for defect detection for the second structure. It

can be concluded that time domain analyses generallyfail while detecting a defect. The envelope method is

employed for velocity and acceleration signals calcu-lated for point P2. It can be observed from Fig. 19 that

the ratio of the envelope spectrum energy is an important

indicator for defect detection even at high speeds, in con-trast with the results for the first structure. The selectionof the velocity or acceleration signal for processing is

unimportant. The presented results indicate that the

geometry of the bearing structure has considerable effect

on the selection of the signal processing parameters or

techniques in condition monitoring applications. Theproposed method can be employed to determine the opti-

mum receiving point and the optimum signal processing

method for a bearing structure in industrial applications.

7. Effect of the loading

There are basically three different ways of loading in

machinery: constant amplitudeconstant direction(gearing forces), constant amplitudevarying direction(unbalanced forces) and varying amplitudevaryingdirection (joint forces). The second form is considered in

the previous sections. The third type of loading, varying

amplitudevarying direction, is also frequently encoun-tered in applications and hence is analyzed with regard

to its effect on the defect detection methods in this sec-tion. The main bearing of a two-stroke engine shown inFig. 20a is considered and vibration analysis is perfor-

med with or without defect. The first structure is usedas the bearing structure. The dimensions and material

properties of the mechanism are given in Table 1.

The gas force acting on the piston is calculated from

a typical indicator diagram of a two-stroke engine and

Fig. 19. Band energy ratio for the new model.


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Fig. 20. (a) Two-stroke engine; (b) gas force; (c) bearing forces subjected to housing.

Table 1

Material properties and dimensions of the mechanism

Component no Material Mass (kg) Length (m) Inertia (Igzz) (kg m2)

2 Steel r = 7860 kg/m3 1.0143 0.065 0.0016183 Steel r = 7860 kg/m3 0.5717 0.2 0.0024474 Aluminum r = 2300 kg/m3 0.2353

can be seen in Fig. 20b. The analysis is performed from

1000 to 5000 rpm and this speed range is suitable for

two-stroke engines. The bearing forces are shown in Fig.

20c for different crank speeds. The gas force is assumed

to be constant for all crank speeds. The radial bearing

force acts on only the lower part of the bearing structureat 1000 rpm and moves around the circumference of the

bearing at other speeds. The amplitude of the bearing

force varies during one cycle of crank rotation and

dynamic modeling has a more complex form. The

location of the defect at 1000 rpm is predicted to be 270 according to the results of the dynamic stress analysis.Vibration analysis is carried out for healthy and defected

cases and defect detection is performed both in time and

frequency analysis. Time domain parameters for velocity

signal are shown in Fig. 21. It is observed from Fig. 21

that the point P2 is the most sensitive receiving point toa defect at 270. The RMS ratios calculated for point P2can be used to detect the defect for all speeds because

the ratio is greater than 2. The crest factor ratios can be

used until 3000 rpm for point P2 and the kurtosis ratios

can be used until 3000 rpm for all the receiving points.

A comparison between the statistical parameters of the

velocity signal calculated at point P2 for unbalanced

force and engine bearing force loading cases shows the

effect of the loading on the defect detection. It is

observed from Fig. 22 that the change in the loading

type affects mostly the RMS ratios and the RMS ratiosbecame a good fault indicator. The crest factor and kur-

tosis ratios are less influenced by the changes in the load-ing. But for both loading types, the point P2 seems to

be the best receiving point. This analysis is extended by

the frequency domain analysis. Fig. 23 is drawn for the

HFRT results. The high frequency resonance technique

is employed for the velocity signals at point P2. It is

observed from Fig. 23 that the band energy ratios are

generally greater than the unbalanced force case except

at 3000 rpm. The band energy ratio has a lower value

at this speed.


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Fig. 21. Statistical properties of velocity signals for two-stroke

engine main bearing: (a) RMS; (b) crest factor; (c) kurtosis.

Fig. 22. Percentage difference in the ratios of the statistical para-

meters.

Fig. 23. Band energy ratio for the two-stroke engine bearing.

8. Conclusions

A technique is proposed to simulate the vibration of

bearing structures which houses a ball bearing with orwithout a defect. A computer program is developed to

model the dynamic loading of the bearing structure, con-

sidering the bearing kinematics and load distribution. A

force model is proposed to simulate the impulse force

which arises at balldefect impact. The nodal excitationsin time domain are defined as an input to a commercialfinite element package. Time and frequency domainanalyses are the most popular methods in condition

monitoring applications. The differences in the vibration

signals of healthy and defected bearings are investigated

by employing these two methods. Time domain para-

meters RMS, crest factor and kurtosis are analyzed with

regard to their effectiveness in defect detection. The high

frequency resonance technique (HFRT) is employed in

the frequency domain analysis and its success is investi-gated. The effect of the rotational speed, geometry of

the bearing structure and type of loading on the selection

of the signal processing technique is illustrated by the

vibration analyses for two different bearing structures

and two different loading conditions at different

rotational speeds. It is observed that both the time and

frequency domain techniques are sensitive to changes in

rotational speeds, structure geometry and loading type.The proposed method can be used to determine the ideal

sensor position and signal processing technique, con-

sidering the rotational speeds, structural effects and load-ing conditions at the beginning of an industrial condition

monitoring application.

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Documents

Kiral 2003 Tribology-International