Al Ghamd 2006 Mechanical Systems and Signal Processing

7/28/2019 Al Ghamd 2006 Mechanical Systems and Signal Processing

1/35

Mechanical Systems

and

Signal ProcessingMechanical Systems and Signal Processing 20 (2006) 15371571

A comparative experimental study on the use of acoustic

emission and vibration analysis for bearing defect identification

and estimation of defect size

Abdullah M. Al-Ghamda, David Mbab,

aMechanical Services Shops Department, Saudi Aramco, Dhahran, Saudi ArabiabSchool of Engineering, Cranfield University, Building 52, Cranfield, Bedfordshire MK43 0AL, UK

Received 27 July 2004; received in revised form 20 October 2004; accepted 24 October 2004

Available online 19 February 2005

Abstract

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique

for rotating machinery. The application of acoustic emission (AE) for bearing diagnosis is gaining ground

as a complementary diagnostic tool, however, limitations in the successful application of the AE techniquehave been partly due to the difficulty in processing, interpreting and classifying the acquired data.

Furthermore, the extent of bearing damage has eluded the diagnostician. The experimental investigation

reported in this paper was centred on the application of the AE technique for identifying the presence and

size of a defect on a radially loaded bearing. An experimental test rig was designed such that defects of

varying sizes could be seeded onto the outer race of a test bearing. Comparisons between AE and vibration

analysis over a range of speed and load conditions are presented. In addition, the primary source of AE

activity from seeded defects is investigated. It is concluded that AE offers earlier fault detection and

improved identification capabilities than vibration analysis. Furthermore, the AE technique also provided

an indication of the defect size, allowing the user to monitor the rate of degradation on the bearing;

unachievable with vibration analysis.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Acoustic emission; Bearing defect; Condition monitoring; Defect size; Vibration analysis

ARTICLE IN PRESS

www.elsevier.com/locate/jnlabr/ymssp

0888-3270/$ - see front matter r 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ymssp.2004.10.013

Corresponding author. Tel.: +44 1234 754681.

E-mail address: [email protected] (D. Mba).
http://www.elsevier.com/locate/jnlabr/ymssphttp://www.elsevier.com/locate/jnlabr/ymssp


2/35

1. Introduction

Acoustic emissions (AEs) are defined as transient elastic waves generated from a rapid release

of strain energy caused by a deformation or damage within or on the surface of a material [1]. Inthis particular investigation, AEs are defined as the transient elastic waves generated by the

interaction of two surfaces in relative motion. The interaction of surface asperities and

impingement of the bearing rollers over the seeded defect on the outer race will generate AEs. Due

to the high-frequency content of the AE signatures typical mechanical noise (less than 20 kHz) is

eliminated.

2. Bearing defect diagnosis and AEs

There have been numerous investigations reported on applying AE to bearing defect diagnosis.Roger [2] utilised the AE technique for monitoring slow rotating anti-friction slew bearings on

cranes employed for gas production. In addition, successful applications of AE to bearing

diagnosis for extremely slow rotational speeds have been reported [3,4]. Yoshioka and Fujiwara

[5,6] have shown that selected AE parameters identified bearing defects before they appeared in

the vibration acceleration range. Hawman et al. [7] reinforced Yoshiokas observation and noted

that diagnosis of defect bearings was accomplished due to modulation of high-frequency AE

bursts at the outer race defect frequency. The modulation of AE signatures at bearing defect

frequencies has also been observed by other researchers [810]. Morhain et al. [11] showed

successful application of AE to monitoring split bearings with seeded defects on the inner and

outer races.

This paper investigates the relationship between AE r.m.s. amplitude and kurtosis for a rangeof defect conditions, offering a more comparative study than is presently available in the public

domain. Moreover, comparisons with vibration analysis are presented. The source of AE from

seeded defects on bearings, which has not been investigated to date, is presented showing

conclusively that the dominant AE source mechanism for defect conditions is asperity contact.

Finally, a relationship between the defect size and AE burst duration is presented, the first known

detailed attempt.

3. Experimental test rig and test bearing

The bearing test rig employed for this study had an operational speed range of 104000 rpm

with a maximum load capability of 16 kN via a hydraulic ram. The test bearing employed was a

Cooper split-type roller bearing (01B40MEX). The split-type bearing was selected as it allowed

defects to be seeded onto the races, furthermore, assembly and disassembly of the bearing was

accomplished with minimum disruption to the test sequence, see Fig. 1. Characteristics of the test

bearing (Split Cooper, type 01C/40GR) were:

Internal (bore) diameter, 40 mm.

External diameter, 84 mm.

ARTICLE IN PRESS

A.M. Al-Ghamd, D. Mba / Mechanical Systems and Signal Processing 20 (2006) 153715711538


3/35

Diameter of roller, 12 mm.

Diameter of roller centers, 166 mm.

Number of rollers, 10.

Based on these geometric properties the outer race defect frequency was determined at 4.1X (4.1

times the rotation shaft speed). The layout of the test rig is illustrated in Fig. 2, with the load zoneat top-dead-centre.

4. Data-acquisition system and signal processing

The transducers employed for vibration and AE data acquisition were placed directly on the

housing of the bearing, see Fig. 1. A piezoelectric-type AE sensor (Physical Acoustic Corporation

type WD) with an operating frequency range of 1001000 kHz was employed whilst a resonant-

type accelerometer, with a flat frequency response between 10 and 8000 Hz (Model 236 Isobase

ARTICLE IN PRESS

Fig. 1. Bearing test rig.

Fig. 2. Test rig layout.

A.M. Al-Ghamd, D. Mba / Mechanical Systems and Signal Processing 20 (2006) 15371571 1539


4/35

accelerometer, Endevco Dynamic Instrument Division) was used for vibration measurement.

Pre-amplification of the AE signal was set at 40 dB. The signal output from the pre-amplifier was

connected (i.e. via BNC/coaxial cable) directly to a commercial data-acquisition card. The

broadband piezoelectric transducer was differentially connected to the pre-amplifier so as toreduce electromagnetic noise through common mode rejection. This acquisition card provided a

sampling rate of up to 10 MHz with 16-bit precision giving a dynamic range of more than 85 dB.

In addition, anti-aliasing filters (100 kHz1.2 MHz) were built into the data acquisition card. A

total of 256,000 data points were recorded per acquisition (data file) at a sampling rates of 2 MHz,

8 and 10 MHz, dependent on simulation. Twenty (20) data files were recorded for each simulated

case, see experimental procedure. The acquisition of vibration data was sampled at 2.5 kHz for a

total of 12,500 data points.

Whilst numerous signal processing techniques are applicable for the analysis of acquired data,

the authors have opted for simplicity in diagnosis, particularly if this technique is to be readily

adopted by industry. The AE parameters measured for diagnosis in this particular investigationwere amplitude, r.m.s. and kurtosis. These were compared with identical parameters from

vibration data. It is worth stating that the selected parameters for AE diagnosis are also typical

for vibration analysis.

The most commonly measured AE parameters for diagnosis are amplitude, r.m.s. energy,

counts and events [12]. Counts involve determining the number of times the amplitude exceeds a

preset voltage (threshold level) in a given time and gives a simple number characteristic of the

signal. An AE event consists of a group of counts and signifies a transient wave. Tan [13] cited a

couple of drawbacks with the conventional AE count technique. This included dependence of the

count value on the signal frequency. Secondly, it was commented that the count rate was

indirectly dependent upon the amplitude of the AE pulses.

By far the most prominent method for vibration diagnosis is the Fast Fourier Transform. Thishas the advantage that a direct association with the characteristics of rotating machine can be

obtained. Other vibration parameters include peak-to-peak, zero-to-peak, r.m.s. crest factor

and kurtosis. The drawback with amplitude parameters is that they can be influenced by phase

changes and spurious electrical spikes. The kurtosis value increases with bearing defect severity

however, as severity worsens the kurtosis value can reduce. The r.m.s. parameter is a measure of

the energy content of the signal and is seen as a more robust parameter. However, whilst r.m.s.

may show marked increases in vibration with degradation, failures can occur with only a slight

increase or decrease in levels. For this reason r.m.s. measurement alone is sometimes insufficient.

5. Experimental procedure

Two test programmes were undertaken.

An investigation to ascertain the primary source of AE activity from seeded defects on bearings

was undertaken, in addition to determining the relationship between defect size, AE and

vibration activity. In an attempt to identify the primary source of AE activity, a surface

topography (Form Talysurf 120L; stylus used had a 2 mm radius diamond tip) of the various

defects was taken. Furthermore, two types of defect conditions were simulated; firstly, a seeded

ARTICLE IN PRESS



5/35

defect with a surface discontinuity that did not result in material protruding above the average

surface roughness of the outer race. The second defect type resulted in material protrusions that

were clearly above the average surface roughness.

The second test programme aimed to establish a correlation between AE activity withincreasing defect size. This was accomplished by controlled incremental defect sizes at a fixed

speed.

Prior to defect simulations for all test programmes, baseline, or defect free, recordings were

undertaken for 12 running conditions; four speed (600, 1000, 2000 and 3000 rpm) and three load

conditions (0.1, 4.43 and 8.86 kN). Defects were simulated with the use of an engraving machining

employing a carbide tip.

6. Test programme 1: AE source identification and defects of varying severities

Five test conditions of varying severities were simulated on the outer race of the test bearing

which was positioned in the load zone; top-dead-centre for this particular test-rig configuration,

see Table 1. In addition, the nomenclature used to label all test conditions is detailed in Table 1.

The test conditions were:

Baseline or defect-free condition in which the bearing was operated with no defect on the outer-

race. Fig. 3 shows a visual condition of the race and a surface roughness map (maximum

0.5mm).

A point defect engraved onto the outer race which was approximately 0.85 0.85 mm2, see

Fig. 4. This defect condition had material of the outer race protruding approximately 4 mm

above the bearing maximum surface roughness.

A line defect, approximately 5.6 1.2 mm2, see Fig. 5.

A rough defect, approximately 17.5 9.0 mm2, see Fig. 6.

A smooth defect in which a surface discontinuity, not influencing the average surface

roughness, was simulated. In this particular instance a grease hole on the outer race matched

the requirements, see Fig. 7. From Fig. 7 it is evident that the point of discontinuity of the

surface does not have a protrusion above the surface roughness as evident for the point or

line defect conditions. The main purpose of this simulation is to observe if any changes in the

ARTICLE IN PRESS

Table 1Notation for test programme 1 with seeded defect dimensions

Test programme-1: defects

of different severities

Defect type (WL) mm2 Speed (rpm) Load (kN)

N Noise (No defect) S1 600 L0 0.1

SD Smooth defect S2 1000 L1 4.43

PD Point defect (0.850.85) S3 2000 L2 8.86

LD Line defect (5.6 1.2) S4 3000

RD Big rough defect (17.59.0)



6/35

load distribution will lead to generation or changes in AE activity in comparison to a defect-free

condition.

All defects were run under four speeds (600, 1000, 2000 and 3000 rpm) and three different loads

(0.1, 4.43 and 8.86 kN). It must be noted that the defect length is along the race in the direction of

the rolling action and the defect width is across the race.

7. Test programme 2: defects of varying size

For this particular test programme, two experiments (E1 and E2) were carried out to

authenticate observations relating AE to varying defect sizes. Each experiment included seven

ARTICLE IN PRESS

-4.00E-03

-3.00E-03

-2.00E-03

-1.00E-03

0.00E+00

1.00E-03

2.00E-03

3.00E-03

4.00E-03

5.00E-03

Distance Along the Race

Millimeter

0.85 mm

Fig. 4. Surface profile of point defect condition.

-2.00E-03

-1.50E-03

-1.00E-03

-5.00E-04

0.00E+00

5.00E-04

1.00E-03

1.50E-03

2.00E-03


Millimeters

Fig. 3. Surface profile of defect-free condition.



7/35

defects of different lengths and widths, see Table 2. A sample defect is shown in Fig. 8. In

Experiment 1 (E1), a point defect (D1) was increased in length in three steps (D2D4) and then

increased in width in three more steps (D5D7). However, in Experiment 2 (E2), a point defect

was increased in width and then in length interchangeably from D1 to D7. Both experiments wererun at 2000 rpm and at a load of 4.4 kN. The AE sampling rates for the first and second

experiments are 8 and 10 MHz, respectively. In Experiment 2, vibration was acquired in addition

to AE for comparative purposes.

8. Analysis procedure

If the defects simulated were to produce AE transients, as each rolling element passed the

defect, it was envisaged that the AE bursts would be detected at a rate equivalent to the outer race

ARTICLE IN PRESS

Fig. 6. Rough defect condition.

-2.50E-03

-2.00E-03

-1.50E-03

-1.00E-03

-5.00E-04

0.00E+00

5.00E-04

1.00E-03

1.50E-03

2.00E-03


Millimeters

1.2 mm

Fig. 5. Surface profile of a line defect condition.



8/35

defect frequency (4.1X). In addition, it was also anticipated that the defect frequency would be

observed in the vibration frequency range.

For test programme 1 (defects of different severities) AE in time domain and vibrations in

frequency domain were analysed. Furthermore, the AE and vibration r.m.s. maximum amplitude

and kurtosis values were calculated. Approximately 20 AE data files were captured per fault

simulated. Each data file was equivalent to one, two, four and six revolutions at speeds of 600,

1000, 2000 and 3000 rpm, respectively. Every AE data file (for all simulations) was broken into

sections equivalent to one revolution. For instance, at 2000 rpm the AE data was split into four

equal sections, each representing one shaft revolution.

ARTICLE IN PRESS

Table 2

Notation for test programme 2 with seeded defect dimensions

Test programme 2: defects of different sizes

Experiment 1 Experiment 2

Defect size (width length) mm2 Defect size (width length) mm2

E1-D0 No defect E2-D0 No defect

E1-D1 0.85 0.85 E2-D1 0.85 1.35

E1-D2 12.95 E2-D2 2. 1.35

E1-D3 17.12 E2-D3 24

E1-D4 115.83 E2-D4 44

E1-D5 3.9815.83 E2-D5 84

E1-D6 8.6615.83 E2-D6 134

E1-D7 13.615.83 E2-D7 1310

-1.00E-02

-8.00E-03

-6.00E-03

-4.00E-03

-2.00E-03

0.00E+00

2.00E-03

4.00E-03

6.00E-03


Millimeter

Defect (Hole) Starts Here

Edge of Defect is Smooth

Fig. 7. Surface profile of a smooth defect condition.



9/35

The diagnostic parameters of r.m.s., etc. were calculated for each shaft revolution and averaged

for all data files. This implied that at 2000 rpm, and for 20 data files, a total of 80 AE r.m.s. values

were calculated and the value presented is the average of the eight values. For vibration analysis,

two data files were acquired for each simulation. This was equivalent to 50, 83, 166 and 250

revolutions per data file at speeds of 600, 1000, 2000 and 3000 rpm, respectively. The exact

procedure for calculating the AE parameters was employed on the vibration data.

For test programme 2 observations of AE burst duration, r.m.s. and amplitude for the various

defect sizes were undertaken. The burst duration was obtained by calculating the duration fromthe point at which the AE response was higher than the underlying background noise level to the

point at which it returned to the underlying noise level. This procedure was undertaken for every

data file and the average value for each simulated case is presented.

9. Data analysis: test programme 1

9.1. Observations of AE time waveform

As already stated the outer race defect frequency was calculated for the various test speeds; 41,69, 137 and 205 Hz at 600, 1000, 2000 and 3000 rpm, respectively. Typical AE and vibration time

waveforms for two different conditions are displayed in Figs. 9 and 10. It was noted that for all

defects conditions, other than the smooth defect, AE burst activity was noted at the outer race

defect frequency. Observations of AE time waveforms from noise and smooth defect conditions

showed random AE bursts that occurred at a rate which could not be related to any machine

phenomenon. Correspondingly, such transient bursts associated with the presence of the defect

were not observed on vibration waveforms, but more interestingly, the outer race defect frequency

was not observed on the frequency spectra of most vibration data, except at one defect condition;

rough defect, 3000 rpm, load 0.1 kN, see Fig. 11.

ARTICLE IN PRESS

Fig. 8. The largest seeded defect, E1-D7, 13.615.83 mm2.



10/35

9.2. Observations of r.m.s. values

The r.m.s. values of AE and vibration signatures for all defect and defect-free conditions

were compared for increasing speeds, see Figs. 12 and 13. For all test conditions, the AE r.m.s.

value increased with increasing the speed at a fixed load. It was also noted that the AE r.m.s.

values of noise and smooth defect were similar while AE r.m.s. values increased with increased

defect severity; point, line and rough defects, respectively. For a fixed speed and variable

load it was observed that in general AE r.m.s. increased with load, which increased also withincreasing defect severity, see Fig. 28 of Appendix B. The vibration r.m.s. values showed a

relatively small increase with increasing defect size, however, a clear increase in vibration

r.m.s. was observed for the rough defect, see Fig. 13. Table 6 in Appendix A highlights the

percentage change of r.m.s. values for different defect sizes, emphasising the sensitivity of AE to

defect size progression, see Figs. 14 and 15. The percentage values presented in Appendix A

and Figs. 14 and 15 were obtained by relating all speed and load defect simulations

to the corresponding speed and load condition for the defect-free (noise) simulation. Figs. 28

and 29 of Appendix B show the vibration and AE r.m.s. values for increasing loads at

fixed speeds.

ARTICLE IN PRESS

Fig. 9. AE time waveforms for all defects at a speed of 1000 rpm and a load 4.43 kN.



11/35

9.3. Observations of maximum amplitude

It was noted that AE max amplitude increased with increasing speed for a fixed load, see

Fig. 16. Also it was evident that as the defect size was increased, the maximum AE amplitude

increased. The maximum AE amplitude increased from noise condition to the point defect and

increased further for the line defect. The maximum amplitude for the rough defect was

comparable to the line defect. Again it was noted that values for noise and smooth conditions

were similar. Vibration amplitude values were similar for all defect conditions except for the

rough defect, see Fig. 17. Table 7 in Appendix A highlights the percentage changes of maximumamplitude values for different defect sizes. These were determined as detailed in the previous

section. Figs. 30 and 31 of Appendix B show the vibration and AE maximum amplitude values for

increasing loads at fixed speeds.

9.4. Observations of kurtosis

Kurtosis is a measure of the peakness of a distribution and is widely established as a good

indicator of bearing health for vibration analysis. For a normal distribution, kurtosis is equal to 3.

The AE kurtosis values for the noise signal (N) and the smooth defect (SD) were approximately

ARTICLE IN PRESS

Fig. 10. Vibration time waveforms for all defects at a speed of 1000 rpm and a load 4.43 kN.



12/35

3, see Fig. 18, as expected for a random distribution. It was noted that as defect size increased

from noise to point to line, the kurtosis values increased accordingly. For the worst defect

condition it was observed that the kurtosis values were lower than for the preceding defect

condition (line defect). This was not unexpected because as the defect condition worsens it is

known that kurtosis values will decrease; Fig. 18 depicts this observation. Kurtosis results for

ARTICLE IN PRESS

Fig. 11. Sample vibration data in the frequency domain.



13/35

vibration showed similar values for all defect simulations apart from the rough defect where a

relative increase was noted, see Fig. 19. Table 8 in Appendix A highlights the change of kurtosis

values for different defect sizes, emphasising the sensitivity of AE to defect size progression. Figs. 32

and 33 of Appendix B show the vibration and AE kurtosis values for increasing loads at fixed speeds.

10. Data analysis: test programme 2

The analysis of this test programme was centred on AE time-domain observations.

ARTICLE IN PRESS

1.00E-03

1.00E-02

1.00E-01

1.00E+00

S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4

AEr.m.s.

(volts)

L0

L1

L2

N SD PD LD RD

Fig. 12. AE r.m.s. of different defects at increasing speeds at a fixed load.

0.10

1.00

10.00


Vibrationr.m.s.(volts)

L0

L1

L2

N SD PD LD RD

Fig. 13. Vibration r.m.s. of different defects at increasing speeds at a fixed load.



14/35

10.1. Observations of Experiment 1 (E1)

This experiment included seven defect conditions: D1D4 had a fixed width with increasing

length while defects D4D7 had a fixed length with increasing width; see Table 2. From

observations of the AE time waveforms, AE bursts were clearly evident from defects D4D7, see

Fig. 20. The x-axis in these figures corresponds to one shaft revolution at 2000 rpm. For defects

ARTICLE IN PRESS

-40%

160%

360%

560%

760%

960%

1160%

1360%


%c

hangerelativetodefectfreeconditio

n

L0 L1 L2

N SD PD LD RD

Fig. 14. Percentage change in AE r.m.s.

-10%

40%

90%

140%

190%

240%

290%

340%

390%

440%


%c

hangerelativetodefectfreecondition

N SD PD LD RD

L0 L1 L2

Fig. 15. Percentage change in vibration r.m.s.



15/35


16/35

Two conclusions can be drawn, firstly, increasing the defect width increased the ratio of burst

amplitude-to-operational noise (i.e. the burst signal was increasingly more evident above the

operational noise levels, see Fig. 20). Secondly, it was deduced that increasing the defect length

increased the burst duration. To confirm this, a second experiment was performed utilising the

same running conditions but with different defect size combinations. Furthermore, the second test

was undertaken to ensure repeatability, and a new bearing of identical type to that used in

Experiment 1 was employed.

ARTICLE IN PRESS

1.00

10.00

100.00

S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4S1 S2 S3 S4S1 S2 S3 S4

VibrationKurtosisVa

lue

L0

L1

L2

N SD PD LD RD

Fig. 19. Vibration kurtosis of different defects at increasing speeds and fixed load.

1.00E+00

1.00E+01

1.00E+02

1.00E+03


AE

KurtosisValue

L0

L2

L5

N SD PD LD RD

Fig. 18. AE kurtosis of different defects at increasing speeds and fixed load.



17/35

10.2. Observations of Experiment 2 (E2)

The difference between this experiment and that reported in the previous section is two-fold.

Firstly, the defect size and arrangement of the seeded defect progression was different from

Experiment 1 and secondly, the AE data captured was sampled at 10 MHz.

Observations of the bursts durations for defects D1D7, see Fig. 22, identified that for defects

D3D7, the burst duration was discernable; these defects had widths of at least 2 mm. It was also

ARTICLE IN PRESS

Fig. 20. Sample AE time wave forms for defects D0D7 (Experiment 1).



18/35

observed that the AE bursts for defects D3D6 (Fig. 23) were similar; these defects had the same

length of 4mm (Table 4). Clearly, when the defect was increased in length from 4 to 10 mm

(D6D7) the burst duration increased dramatically, see Figs. 23 and 24.

A summary of AE burst duration for Experiments 1 and 2 are detailed in Table 5. From Table

5, a linear relation between the burst duration and the defect length is observed, see Fig. 25. Thevariation of this data about the mean was approximately710%.

Observations of vibration measurements in Experiment 2 of test programme 2 failed to locate

the defect source under all simulations but one condition, D3-L1. This was in contrast to AE. The

r.m.s. and maximum amplitude values for AE and vibrations obtained in test programme 2,

Experiment 2, are detailed in Figs. 26 and 27. These were calculated per data file. Again as

reported in test programme 1, AE r.m.s. increased from defect size D1 onwards whilst AE

maximum amplitude values increased from defect size D3, see Figs. 26 and 27. In contrast

vibration r.m.s. and maximum amplitude values have increased for defects D4 onwards, see

Figs. 26 and 27.

ARTICLE IN PRESS

Fig. 21. Burst duration for defect D6 and D7 (Experiment 1).

Table 3

Burst-to-noise ratios for defects of fixed defect length (15.8 mm)

Defect Burst duration (s) Burst amplitude (V) Noise amplitude (V) Burst-to-noise ratio

D4 0.0061 0.13 0.09 1.4:1D5 0.0056 0.18 0.09 2.0:1

D6 0.0056 0.33 0.11 3.0:1

D7 0.0056 0.46 0.10 4.6:1



19/35

ARTICLE IN PRESS

Fig. 22. Sample AE time wave-forms for defects D0D7 (Experiment 2).

Table 4

Burst-to-noise ratios for defects with a fixed length (4 mm) and a defect (D7) with an increased length (10 mm)

Defect Burst duration (s) Burst amplitude (V) Noise amplitude (V) Signal-to-noise ratio

D3 0.0018 0.17 0.10 1.7:1

D4 0.0018 0.24 0.09 2.7:1

D5 0.0019 0.32 0.10 3.2:1

D6 0.0019 0.43 0.11 3.9:1

D7 0.0036 0.42 0.11 3.8:1



20/35

ARTICLE IN PRESS

Fig. 23. AE waveform bursts for defects D5D6 (Experiment 2).

Fig. 24. AE waveform burst for defect D7 (Experiment 2).

Table 5

Defect length and width vs. burst duration from Experiments 1 and 2

Exp. Defect Length (mm) Width (mm) Burst duration (s)

2 D3D6 4 4, 8 and 13 0.00185

2 D7 10 13 0.00360

1 D5D7 15.83 4, 9 and 14 0.00564



21/35

11. Discussion

The source of AE for seeded defects is attributed to material protrusions above the surface

roughness of the outer race. This was established as the smooth defect could not be distinguished

from the no-defect condition. However, for all other defects where the material protruded above

the surface roughness, AE transients associated with the defect frequency were observed. As the

defect size increased, AE r.m.s. maximum amplitude and kurtosis values increased, however,

observations of corresponding parameters from vibration measurements were disappointing.

Although the vibration r.m.s. and maximum amplitude values did show changes with defect

condition, the rate of such changes highlighted the greater sensitivity of the AE technique to early

ARTICLE IN PRESS

AE Burst Duration vs. Defect Length

0

2

4

6

8

10

12

14

16

18

0.00185 0.00360 0.00564

Burst Duration

DefectLengthinm

m

Variation 10%

Fig. 25. AE burst duration vs. defect length.

0

0.5

1

1.5

2

2.5

3

D1 D2 D3 D4 D5 D6 D7

Defect

Vibration

Max.

Amplitude(Volts)

0.00E+00

1.00E-01

2.00E-01

3.00E-01

4.00E-01

5.00E-01

6.00E-01

7.00E-01

AEMax.

Amplitude(Volts)

Vibration

AE

Fig. 26. AE maximum amplitude values for Experiment 2, defects D1D7.



22/35

defect detection, see Appendix A. Again, unlike vibration measurements, the AE transient bursts

could be related to the defect source whilst the frequency spectrum of vibration readings failed in

the majority of cases to identify the defect frequency or source. Also evident from this

investigation is that AE levels increase with increasing speed and load. It should be noted that

further signal processing could be applied to the vibration data in an attempt to enhance defectdetection. Techniques such as demodulation, band-pass filtering, etc. could be applied though

these were not employed for this particular investigation. The main reason for not applying

further signal processing to the vibration data was to allow a direct comparison between the

acquired AE and vibration signature. From the results presented two important features were

noted; firstly, AE was more sensitive than vibration to variation in defect size, and secondly, that

no further analysis of the AE response was required in relating the defect source to the AE

response, which was not the case for vibration signatures.

The relationship between defect size and AE burst duration is a significant finding. In the longer

term, and with further research, this offers opportunities for prognosis. AE burst duration was

directly correlated to the seeded defect length (along the race in the direction of the rolling action)whilst the ratio of burst amplitude to the underlying operational noise levels was directly

proportional to the seeded defect width.

The variation of all data presented for test programmes 1 and 2 are detailed in Tables 911 in

Appendix C and Tables 1214 in Appendix D. The standard deviation and coefficient of variation

(CV) for all parameters presented in the paper are detailed. The CV is a measure of the relative

dispersion in a set of measurements. From Appendix C, it was noted that the average CV for

r.m.s. maximum amplitude and kurtosis for AE was 17%, 43% and 73%, respectively.

Correspondingly the average CV for equivalent vibration parameters was 11%, 26% and 43%.

This showed that the kurtosis measurements/calculations had a greater variability about the

ARTICLE IN PRESS

0

0.2

0.4

0.6

0.8

1

1.2

D1 D2 D3 D4 D5 D6 D7

Defect

VibrationRMS(Volts)

0.00E+00

2.00E-02

4.00E-02

6.00E-02

8.00E-02

1.00E-01

1.20E-01

AERMS(Volts)

Vibration

AE

Fig. 27. Vibration r.m.s. values for Experiment 2, defects D1D7.



23/35

average value than r.m.s. and maximum amplitude. For test programme 2 the CV was less than

20% for AE parameters and just over 30% for vibration parameters.

12. Conclusion

It has been shown that the fundamental source of AE in seeded defect tests was due to material

protrusions above the mean surface roughness. Also, AE r.m.s. maximum amplitude and kurtosis

have all been shown to be more sensitive to the onset and growth of defects than vibration

measurements. A relationship between the AE burst duration and the defect length has been

presented.

Appendix A

The percentage of r.m.s. values for different defect sizes, emphasising the sensitivity of

AE to defect size progression is shown in Table 6. Similarly, percentage changes in AE,

vibration maximum amplitude values, vibration and AE kurtosis values are shown in Tables 7

and 8.

ARTICLE IN PRESS

Table 6

Percentage changes in vibration and AE r.m.s. values

AE (% change) L0 L1 L2 VIB (% change) L0 L1 L2

N S1 0 0 0 N S1 0 0 0

S2 0 0 0 S2 0 0 0

S3 0 0 0 S3 0 0 0

S4 0 0 0 S4 0 0 0

SD S1 6 22 13 SD S1 5 1 1

S2 20 15 22 S2 6 14 0

S3 141 63 12 S3 4 12 5

S4 150 12 10 S4 16 12 0

PD S1 371 24 27 PD S1 4 4 0

S2 54 7 30 S2 27 3 10

S3 148 89 36 S3 13 34 36S4 194 124 54 S4 72 35 44

LD S1 170 135 35 LD S1 2 23 8

S2 383 210 49 S2 17 26 3

S3 871 590 212 S3 44 52 21

S4 1313 479 344 S4 172 34 34

RD S1 284 223 237 RD S1 134 251 162

S2 371 189 147 S2 161 343 286

S3 843 446 353 S3 192 376 339

S4 1048 504 458 S4 238 172 143



24/35

ARTICLE IN PRESS

Table 7

Percentage changes in AE and vibration maximum amplitude values


N S1 0 0 0 N S1 0 0 0

S2 0 0 0 S2 0 0 0

S3 0 0 0 S3 0 0 0

S4 0 0 0 S4 0 0 0

SD S1 13 23 20 SD S1 3 16 5

S2 11 17 9 S2 6 3 6

S3 90 61 12 S3 10 17 8

S4 75 19 10 S4 9 22 5

PD S1 992 23 41 PD S1 4 8 3

S2 242 72 41 S2 16 4 11

S3 347 319 199 S3 9 30 27

S4 262 205 172 S4 71 37 40LD S1 918 539 262 LD S1 21 27 7

S2 1726 664 275 S2 39 37 10

S3 3250 1839 692 S3 89 67 30

S4 3546 1264 1026 S4 159 44 34

RD S1 1652 732 1250 RD S1 110 302 186

S2 1951 376 378 S2 147 330 301

S3 3681 883 482 S3 209 288 260

S4 2992 754 579 S4 264 155 115

Table 8

Percentage changes in vibration and AE kurtosis values


N S1 0 0 0 N S1 0 0 0

S2 0 0 0 S2 0 0 0

S3 0 0 0 S3 0 0 0

S4 0 0 0 S4 0 0 0

SD S1 6 10 7 SD S1 16 29 18

S2 8 14 23 S2 3 17 7

S3 16 1 19 S3 9 7 22

S4 29 27 10 S4 4 11 18

PD S1 750 158 174 PD S1 18 6 8

S2 328 270 479 S2 3 42 12S3 190 575 664 S3 6 10 15

S4 37 98 336 S4 2 11 20

LD S1 1999 1291 965 LD S1 8 11 20

S2 2303 932 916 S2 142 44 57

S3 1839 1418 1201 S3 175 13 10

S4 974 861 1073 S4 95 25 6

RD S1 3112 864 2995 RD S1 24 84 78

S2 2637 213 328 S2 39 71 43

S3 2136 352 129 S3 31 25 30

S4 837 178 109 S4 25 262 363



25/35

Appendix B

Figs. 28 and 29 show the AE and vibration r.m.s. of different defects for increas-

ing loads at a fixed speed. Similarly, Figs. 30 and 31 show the vibration and AE

ARTICLE IN PRESS

1.00E-03

1.00E-02

1.00E-01

1.00E+00

L0 L1 L2 L0 L1 L2 L0 L1 L2 L0 L1 L2 L0 L1 L2

AEr.m.s.

(vo

lts)

S1 S2 S3 S4

N SD PD LD RD

Fig. 28. AE r.m.s. of different defects at increasing loads at a fixed speed.

0.10

1.00

10.00


Vibrationr.m.s.

(volts)

N SD PD LD RD

S1 S2 S3 S4

Fig. 29. Vibration r.m.s. of different defects at increasing loads at a fixed speed.



26/35

maximum amplitude values for increasing loads at a fixed speed. AE and vibration

kurtosis values of different defects at increasing loads at a fixed speed are shown in Figs. 32

and 33.

ARTICLE IN PRESS

0.01

0.10

1.00

10.00


AEMaximumA

mp

litude(volts)

N SD PD LD PD

S1 S2 S3 S4

Fig. 30. AE max amplitude of different defects at increasing loads and fixed speed fixed load.

0.10

1.00

10.00


VibrationMaxAmplitude(volts)

N SD PD LD RD

S1 S2 S3 S4

Fig. 31. Vibration max amplitude of different defects at increasing loads and fixed speed.



27/35

Appendix C

The variation of all data, i.e. r.m.s., maximum amplitude, and kurtosis for test programme 1 are

presented in Tables 911, respectively. The standard deviation and CV for all parameters

presented in the paper are detailed.

ARTICLE IN PRESS

1.00E+00

1.00E+01

1.00E+02

1.00E+03


AEKurtosisValue

N SD PD LD RD

S1 S2 S3 S4

Fig. 32. AE kurtosis of different defects at increasing loads and fixed speed.

1.00

10.00

100.00


VibrationKurtosisValue

N SD PD LD RD

S1 S2 S3 S4

Fig. 33. Vibration kurtosis of different defects at increasing loads and fixed speed.



28/35

ARTICLE IN PRESS

Table9

Mean,standarddeviationand

coefficientofvariationforalltestconditions:r.m.s.

Key:-

Mean

S.

De

viation

CoefficientofVariation%

AERMS

L0

L1

L2

VibrationRMS

L0

L1

L2

N

S1

0.0

0208

0.0

0007

0.0

0421

0.0

0015

0.0

0573

0.00

016

N

S1

0.1

105340.0049810.1

089380.0

051640.1

1247

60.0

0545

3.5

7%

3.4

5%

2.7

6%

4.5

1%

4.7

4%

4.8

5%

S2

0.0

0442

0.0

0021

0.0

0995

0.0

0079

0.0

145970.00

0901

S2

0.1

404870.0137680.1

7917

0.0

146760.1

7300

50.0

15096

4.7

7%

7.9

1%

6.1

7%

9.8

0%

8.1

9%

8.7

3%

S3

0.0

1031

0.0

03509

0.0

198740.0

009730.0

323910.00

0954

S3

0.2

58820.0495970.3

819940.0

457410.3

7541

60.0

42903

34.0

3%

4.9

0%

2.9

4%

19.1

6%

11.9

7%

11

.43%

S4

0.0

149350.0

02854

0.0

351250.0

011320.0

5129

0.00

1684

S4

0.2

623330.0321620.6

479260.0

983110.6

6033

70.0

94787

19.1

1%

3.2

2%

3.2

8%

12.2

6%

15.1

7%

14

.35%

SD

S1

0.0

019436.2

6E-05

0.0

0329

0.0

001610.0

050190.00

0193SD

S1

0.1

160160.0057960.1

095620.0

041740.1

1312

90.0

05431

3.2

2%

4.9

1%

3.8

4%

5.0

0%

3.8

1%

4.8

0%

S2

0.0

053250.0

00298

0.0

085060.0

005120.0

113520.00

0407

S2

0.1

31380.0084130.2

044070.0

175750.1

7294

0.0

1648

5.5

9%

6.0

1%

3.5

9%

6.4

0%

8.6

0%

9.5

3%

S3

0.0

249210.0

01294

0.0

325010.0

012630.0

362850.00

1614

S3

0.2

478240.0312480.4

2684

0.0

629430.3

5813

70.0

41745

5.1

9%

3.8

9%

4.4

5%

12.6

1%

14.7

5%

11

.66%

S4

0.0

372820.0

06864

0.0

393630.0

082590.0

564660.00

7488

S4

0.3

035620.0484520.7

240740.1

012850.6

5741

0.0

87132

18.4

1%

20.9

8%

13.2

6%

15.9

6%

13.9

9%

13

.25%

PD

S1

0.0

097980.0

09436

0.0

031970.0

002210.0

0421

0.00

0218PD

S1

0.1

147640.0110170.1

129680.0

065250.1

1243

90.0

05111

96.3

0%

6.9

2%

5.1

9%

9.6

0%

5.7

8%

4.5

5%

S2

0.0

067920.0

08873

0.0

092080.0

009130.0

101530.00

1203

S2

0.1

777510.0196470.1

744590.0

1839

0.1

5568

80.0

12463



29/35

ARTICLE IN PRESS

130.6

4%

9.9

2%

11.8

4%

11.0

5%

10.5

4%

8.0

1%

S3

0.0

256030.0

09668

0.0

375510.0

043840.0

439650.00

5116

S3

0.2

925890.02911

0.5

126180.0

594010.5

1178

30.0

67242

37.7

6%

11.6

7%

11.6

4%

9.9

5%

11.5

9%

13

.14%

S4

0.0

438980.0

04357

0.0

789590.0

063220.0

788060.01

2878

S4

0.4

511930.0536420.8

770460.1

411660.9

5326

60.1

13108

9.9

3%

8.0

1%

16.3

4%

11.8

9%

16.1

0%

11

.87%

LD

S1

0.0

056120.0

00734

0.0

099230.0

014160.0

077470.00

1341LD

S1

0.1

083930.0041470.1

342010.0

107030.1

2141

50.0

07239

13.0

8%

14.2

7%

17.3

1%

3.8

3%

7.9

8%

5.9

6%

S2

0.0

212960.0

04553

0.0

308480.0

0833

0.0

2165

0.00

5113

S2

0.1

648990.0200480.2

260540.0

244050.1

7817

20.0

1988

21.3

8%

27.0

0%

23.6

2%

12.1

6%

10.8

0%

11

.16%

S3

0.1

002210.0

3314

0.1

373790.0

361580.1

010170.02

7554

S3

0.3

723720.0732070.5

818650.0

671040.4

5522

20.0

67104

33.0

7%

26.3

2%

27.2

8%

19.6

6%

11.5

3%

14

.74%

S4

0.2

103380.0

68644

0.2

035990.0

529270.2

278060.07

2744

S4

0.7

141180.1925180.8

660220.1

5571

0.8

8366

20.1

68333

32.6

3%

26.0

0%

31.9

3%

26.9

6%

17.9

8%

19

.05%

RD

S1

0.0

0795

0.0

01792

0.0

135710.0

018020.0

193340.00

1616RD

S1

0.2

582670.0232910.3

818820.0

463620.2

9481

0.0

43323

22.5

4%

13.2

8%

8.3

6%

9.0

2%

12.1

4%

14

.70%

S2

0.0

208540.0

06258

0.0

287610.0

035590.0

361570.00

1746

S2

0.3

666870.0331270.7

930270.0

672340.6

6747

80.0

66637

30.0

1%

12.3

7%

4.8

3%

9.0

3%

8.4

8%

9.9

8%

S3

0.0

969470.0

24285

0.1

0851

0.0

1382

0.1

466290.00

7698

S3

0.7

546060.0900481.8

167250.1

788971.6

4329

30.1

90666

25.0

5%

12.7

4%

5.2

5%

11.9

3%

9.8

5%

11

.60%

S4

0.1

710960.0

314

0.2

120820.0

179320.2

857690.01

2569

S4

0.8

871290.12968

1.7

632340.1

217821.6

0711

50.1

18789

18.3

5%

8.4

6%

4.4

0%

14.6

2%

6.9

1%

7.3

9%



30/35

ARTICLE IN PRESS

Table10


coefficientofvariationforalltestconditions:maximumamplitude

Key:-

Mean

S.

De

viation


AEmax.

L0

L1

L2

Vibrationmax.

L0

L1

L2

amplitude

amplitude

N

S1

0.0

149

0.0

06524

0.0

368030.0

201280.0

4131

0.01

1605N

S1

0.4

261220.1045720.3

7851

0.0

944750.3

7609

20.0

67449

43.7

8%

54.6

9%

28.0

9%

24.5

4%

24.9

6%

17

.93%

S2

0.0

295690.0

13988

0.1

021480.0

654220.1

085970.02

7766

S2

0.4

554510.1146070.5

892010.1

416210.5

7306

10.1

56391

47.3

1%

64.0

5%

25.5

7%

25.1

6%

24.0

4%

27

.29%

S3

0.0

607250.0

28089

0.1

3416

0.0

326670.2

217150.04

9594

S3

0.6

845090.1693951.0

656830.3

155761.1

2158

50.3

91334

46.2

6%

24.3

5%

22.3

7%

24.7

5%

29.6

1%

34

.89%

S4

0.0

950590.0

31538

0.2

366970.0

657

0.3

3793

0.07

6411

S4

0.6

378550.1234351.5

0238

0.4

006721.5

4775

50.4

9042

33.1

8%

27.7

6%

22.6

1%

19.3

5%

26.6

7%

31

.69%

SD

S1

0.0

167670.0

0644

0.0

282750.0

0826

0.0

333630.00

6056SD

S1

0.4

114690.0758820.3

186430.0

660420.3

56

0.0

67684

38.4

1%

29.2

1%

18.1

5%

18.4

4%

20.7

3%

19

.01%

S2

0.0

336160.0

13939

0.0

847820.0

510910.0

976530.03

2219

S2

0.4

266830.0867090.6

097010.1

246840.5

4152

40.1

08176

41.4

7%

60.2

6%

32.9

9%

20.3

2%

20.4

5%

19

.98%

S3

0.1

153350.0

17671

0.2

150260.0

5394

0.2

500930.07

7732

S3

0.6

142410.1183721.2

5011

0.4

252671.0

2750

90.3

04789

15.3

2%

25.0

9%

31.0

8%

19.2

7%

34.0

2%

29

.66%

S4

0.1

664210.0

32748

0.2

818030.0

6331

0.3

7256

0.06

8917

S4

0.6

942920.1494751.8

3638

0.4

871891.6

2518

0.4

56532

19.6

8%

22.4

7%

18.5

0%

21.5

3%

26.5

3%

28

.09%

PD

S1

0.1

649540.1

58003

0.0

453070.0

184770.0

590050.02

3613PD

S1

0.4

085310.1204790.3

467240.0

718260.3

6326

50.0

71478

95.7

9%

40.7

8%

40.0

2%

29.4

9%

20.7

2%

19

.68%



31/35

ARTICLE IN PRESS

S2

0.1

056950.1

75902

0.1

779650.0

685970.1

5174

0.07

4084

S2

0.5

286830.1310540.6

151830.2

128180.5

1187

80.1

4524

166.4

2%

38.5

5%

48.8

2%

24.7

9%

34.5

9%

28

.37%

S3

0.2

721270.1

80325

0.5

636620.2

1607

0.6

767730.23

8314

S3

0.7

484150.1265571.3

842290.4

118691.4

2298

50.4

72924

66.2

7%

38.3

3%

35.2

1%

16.9

1%

29.7

5%

33

.23%

S4

0.3

438040.1

07785

0.7

273290.2

591290.9

226970.43

2785

S4

1.0

896180.2067672.0

602980.5

243592.1

6636

10.5

71468

31.3

5%

35.6

3%

46.9

0%

18.9

8%

25.4

5%

26

.38%

LD

S1

0.1

5155

0.0

49152

0.2

3904

0.1

184430.1

507890.10

6757LD

S1

0.3

359390.0811270.4

821530.1

191560.4

0098

0.0

8549

32.4

3%

49.5

5%

70.8

0%

24.1

5%

24.7

1%

21

.32%

S2

0.5

499660.2

03333

0.7

933670.4

444170.3

997680.28

28

S2

0.6

34

0.2295190.8

062740.2

166810.6

3108

50.1

99209

36.9

7%

56.0

2%

70.7

4%

36.2

0%

26.8

7%

31

.57%

S3

2.0

325341.1

21462

2.6

011241.3

089421.7

699840.93

768

S3

1.2

954390.5788471.7

831130.5

758851.4

5425

60.5

51794

55.1

8%

50.3

2%

52.9

8%

44.6

8%

32.3

0%

37

.94%

S4

3.4

5586

1.8

41971

3.2

3032

1.6

710893.8

334062.09

603

S4

1.6

509060.80296

2.1

661020.7

680992.0

7771

40.8

49431

53.3

0%

51.7

3%

54.6

8%

48.6

4%

35.4

6%

40

.88%

RD

S1

0.2

629690.1

82349

0.3

060430.1

271730.5

649630.10

7298RD

S1

0.8

935710.15583

1.5

222860.3

061731.0

7557

10.2

68539

69.3

4%

41.5

5%

18.9

9%

17.4

4%

20.1

1%

24

.97%

S2

0.6

176840.4

09719

0.4

913570.2

915560.5

186360.10

9704

S2

1.1

256460.2119432.5

365240.4

150322.2

9829

30.4

71199

66.3

3%

59.3

4%

21.1

5%

18.8

3%

16.3

6%

20

.50%

S3

2.3

023430.9

71546

1.3

215080.7

621

1.2

965740.32

8188

S3

2.1

141040.4353894.1

386950.4

255194.0

3531

10.4

48041

42.2

0%

57.6

7%

25.3

1%

20.5

9%

10.2

8%

11

.10%

S4

2.9

349780.9

38397

2.0

180960.7

342772.3

076210.48

1824

S4

2.3

195630.5856913.8

3158

0.6

888023.3

3004

51.5

08469

31.9

7%

36.3

8%

20.8

8%

25.2

5%

17.9

8%

45

.30%



32/35

ARTICLE IN PRESS

Table11


coefficientofvariationforalltestconditions:kurtosis

Key:-

Mean

S.

De

viation


AEkurtosisL0

L1

L2

VibrationkurtosisL0

L1

L2

N

S1

4.4

367824.2

2448

85.0

005626.0

205653.4

6719

0.26

9321N

S1

4.4

367824.2

244885.0

005626.0

205653.4

6719

0.2

69321

95.2

2%

120.4

0%

7.7

7%

95.22

%

120.4

0%

7

.77%

S2

4.3

396193.8

3637

39.9

4077918.6

15363.7

8912

0.59

6502

S2

4.3

396193.8

363739.9

4077918.6

15363.7

8912

0.5

96502

88.4

0%

187.2

6%

15.7

4%

88.40

%

187.2

6%

15.7

4%

S3

3.7

811320.9

2984

84.1

131471.4

252313.8

065020.60

7442

S3

3.7

811320.9

298484.1

131471.4

252313.8

065020.6

07442

24.5

9%

34.6

5%

15.9

6%

24.59

%

34.6

5%

15.9

6%

S4

4.4

362081.8

1598

34.3

685831.3

911684.0

8898

0.90

3627

S4

4.4

362081.8

159834.3

685831.3

911684.0

8898

0.9

03627

40.9

4%

31.8

4%

22.1

0%

40.94

%

31.8

4%

22.1

0%

SD

S1

4.6

659031.4

0439

54.5

058651.0

994533.7

132160.33

158

SD

S1

3.4

189180.6

408172.9

654610.5

7377

3.4

511641.1

20604

30.1

0%

24.4

0%

8.9

3%

18.74

%

19.3

5%

32.4

7%

S2

4.0

058553.3

7472

78.6

0332216.6

46474.6

636312.16

4177

S2

3.5

894270.9

229643.0

272020.6

965433.7

0372

1.4

1759

84.2

4%

193.4

9%

46.4

1%

25.71

%

23.0

1%

38.2

7%

S3

3.1

541070.1

5467

24.0

468151.0

614994.5

1331

3.25

7478

S3

3.3

2605

0.7

709294.2

411291.6

3703

3.9

520451.9

5179

4.9

0%

26.2

3%

72.1

7%

23.18

%

38.6

0%

49.3

9%

S4

3.1

472440.1

4031

25.5

529931.4

739324.5

060461.00

8311

S4

3.1

820820.8

403283.1

061190.8

1242

3.4

085021.0

90185

4.4

6%

26.5

4%

22.3

8%

26.41

%

26.1

6%

31.9

8%

PD

S1

38.8

349133.2

871

212.8

782316.9

22449.5

558577.46

7683PD

S1

4.7

825163.4

975313.9

307971.3

672643.8

7006

41.1

47546

85.7

1%

131.4

0%

78.1

5%

73.13

%

34.7

8%

29.6

5%

S2

20.6

834528.2

239

837.9

749937.6

451221.9

082434.64126

S2

3.8

067491.9

055645.1

581012.6

852324.4

6918

12.2

96074



33/35

ARTICLE IN PRESS

136.4

6%

99.1

3%

158.1

2%

50.06

%

52.0

6%

51.3

8%

S3

11.0

054310.1

247

227.7

603722.0

749

29.8

780421.88277

S3

3.2

335710.8

195654.0

862931.7

244934.2

954111.9

19293

92.0

0%

79.5

2%

73.2

4%

25.35

%

42.2

0%

44.6

8%

S4

6.0

486392.7

3159

38.8

974585.5

4171717.7

852416.71409

S4

2.9

931430.6

634173.1

290640.8

641273.3

434921.1

013

45.1

6%

62.2

8%

93.9

8%

22.16

%

27.6

2%

32.9

4%

LD

S1

92.1

679142.2

220

969.0

294983.0

008937.1

180985.84416LD

S1

3.7

401151.8

923754.6

3935

1.4

100015.0

317094.5

5721

45.8

1%

120.2

4%

231.2

7%

50.60

%

30.3

9%

90.5

7%

S2

105.2

56260.1

931

9105.3

27997.1

980438.2

056761.47599

S2

8.9

801845.9

052585.2

345761.7

076876.3

024494.2

65727

57.1

9%

92.2

8%

160.9

1%

65.76

%

32.6

2%

67.6

8%

S3

73.2

539151.0

954

262.6

246844.0

220449.6

638642.60622

S3

8.3

510774.1

745845.1

467592.0

184365.5

659732.9

8152

69.7

5%

70.3

0%

85.7

9%

49.99

%

39.2

2%

53.5

7%

S4

47.4

169

30.1

159

242.2

689428.9

336248.3

792

35.09275

S4

5.9

452123.3

750894.3

868711.5

837734.3

9866

2.8

13827

63.5

1%

68.4

5%

72.5

4%

56.77

%

36.1

0%

63.9

7%

RD

S1

143.6

338154.7

86

448.0

919948.0

3505107.5

93737.13876RD

S1

5.0

4944

2.1

6377

7.7

260352.6

210857.4

841245.1

98056

107.7

6%

99.8

8%

34.5

2%

42.85

%

33.9

3%

69.4

5%

S2

118.8

80594.6

085

631.5

425444.5

699516.2

45235.00

2033

S2

5.1

576571.7

473926.2

270911.8

959615.7

321171.3

93952

79.5

8%

141.3

0%

30.7

9%

33.88

%

30.4

5%

24.3

2%

S3

84.7

882651.0

130

118.5

256124.5

92738.7

3463

2.48

6449

S3

3.9

826780.9

535393.4

160660.6

297923.5

536740.5

29996

60.1

7%

132.7

5%

28.4

7%

23.94

%

18.4

4%

14.9

1%

S4

41.4

222418.4

248

312.1

30478.9

914148.5

380722.60

9838

S4

3.8

157311.1

8419212.6

59017.1

1176919.2

301710.4

0532

44.4

8%

74.1

2%

30.5

7%

31.03

%

56.1

8%

54.1

1%



34/35

Appendix D

The variation of all data presented for test programme 2 are detailed in Tables 1215. The

standard deviation and CV for all parameters presented in the paper are detailed.

ARTICLE IN PRESS

Table 12

Mean, standard deviation and coefficient of variation for test programme 2: AE r.m.s.

Average 0.03025 0.0339 0.03865 0.0479688 0.06255 0.0728235 0.0981857

Standard deviation 0.0005351 0.0008124 0.0012646 0.0028927 0.0030232 0.0038606 0.0036336

Coefficient of variation (%) 1.77 2.40 3.27 6.03 4.83 5.30 3.70

Load 4.4 kN

D1 D2 D3 D4 D5 D6 D7

Table 13

Mean, standard deviation and coefficient of variation for test programme 2: AE maximum amplitude

Average 0.2066417 0.1776417 0.2297167 0.3562063 0.4710313 0.549 0.6297857



Load 4.4 kN

D1 D2 D3 D4 D5 D6 D7

Table 14

Mean, standard deviation and coefficient of variation for test programme 2: Vibration r.m.s.

Average 0.429082 0.3148242 0.3610585 0.4984004 1.0070777 0.8177705 0.6699566



Load 4.4 kN

D1 D2 D3 D4 D5 D6 D7

Table 15

Mean, standard deviation and coefficient of variation for test programme 2: Vibration maximum amplitude

Average 1.0728039 0.7673333 0.9027242 1.2201647 2.8411781 2.4977007 1.6514511



Load 4.4kN

D1 D2 D3 D4 D5 D6 D7



35/35

References

[1] J.R. Mathews, Acoustic Emission, Gordon and Breach Science Publishers Inc., New York, 1983 (ISSN

0730-7152).[2] L.M. Roger, The application of vibration analysis and acoustic emission source location to on-line condition

monitoring of anti-friction bearings, Tribology International (1979) 5159.

[3] D. Mba, R.H. Bannister, G.E. Findlay, Condition monitoring of low-speed rotating machinery using stress waves:

Parts I and II, Proceedings of the Institution of Mechanical Engineers 213(E) (1999) 153185.

[4] N. Jamaludin, D. Mba, R.H. Bannister, Condition monitoring of slow-speed rolling element bearings using stress

waves. Journal of Process Mechanical Engineering, Institution of Mechanical Engineering: Proceedings of the

Institution of Mechanical Engineers 215(E)(E4) (2001) 245271.

[5] T. Yoshioka, T. Fujiwara, New acoustic emission source locating system for the study of rolling contact fatigue,

Wear 81 (1) (1982) 183186.

[6] T. Yoshioka, T. Fujiwara, Application of acoustic emission technique to detection of rolling bearing failure,

American Society of Mechanical Engineers 14 (1984) 5576.

[7] M.W. Hawman, W.S. Galinaitis, Acoustic emission monitoring of rolling element bearings, Proceedings of the

IEEE, Ultrasonics Symposium (1988) 885889.

[8] T.J. Holroyd, N. Randall, Use of acoustic emission for machine condition monitoring, British Journal of Non-

Destructive Testing 35 (2) (1993) 7578.

[9] T. Holroyd, Condition monitoring of very slowly rotating machinery using AE techniques, 14th International

Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM2001), Manchester,

UK, 46 September 2001, 29 (ISBN 0080440363).

[10] S. Bagnoli, R. Capitani, P. Citti, Comparison of accelerometer and acoustic emission signals as diagnostic tools in

assessing bearing, Proceedings of Second International Conference on Condition Monitoring, London, UK, May

1988, pp. 117125.

[11] A. Morhain, D. Mba, Bearing defect diagnosis and acoustic emission, Journal of Engineering Tribology,

Institution of Mechanical Engineering 217(4) (Part J) (2003) 257272 (ISSN 1350-6501).

[12] J.R. Mathews, Acoustic Emission, Gordon and Breach Science Publishers Inc., New York, 1983 (ISSN

0730-7152).[13] C.C. Tan, Application of acoustic emission to the detection of bearing failures. The Institution of Engineers

Australia, Tribology Conference, Brisbane, 35 December 1990, pp. 110114.

ARTICLE IN PRESS


Al Ghamd 2006 Mechanical Systems and Signal Processing

Documents