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ROLLING ELEMENT BEARING DIAGNOSTICS IN RUN-TO-FAILURE LIFETIME TESTING
Mechanical Systems and Signal Processing (2001) 15(5), 979}993 doi:10.1006/mssp.2001.1418, available online at http://www.idealibrary.com on
ROLLING ELEMENT BEARING DIAGNOSTICS IN RUN-TO-FAILURE LIFETIME TESTING T. WILLIAMS, X. RIBADENEIRA, S. BILLINGTON
AND
T. KURFESS
George W. Woodruw School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, U.S.A. Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failures can be catastrophic and can result in costly downtime. To date, most research has studied crack propagation resulting from arti"cial or &seeded' damage. This damage has been induced in bearings by: scratching the surface, introducing debris into the lubricant or machined with an electrical discharge. The work presented here involves running new undamaged ball and roller bearings through to failure. Traditional vibration metrics such as: root mean square, peak value, kurtosis and crest factor are recorded through the test duration from accelerometers and acoustic emission sensors. 2001 Academic Press
1. INTRODUCTION AND MOTIVATION
Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failures can be catastrophic and can result in costly downtime. Therefore, bearing monitoring poses important challenges to machine maintenance and automation processes. In fact, the importance of bearing condition has resulted in four decades of research on the causes of bearing failure. To date, most research has studied crack propagation resulting from arti"cial or &seeded' damage. This damage has been induced by: scratching the surface, introducing debris into the lubricant or machined with an electrical discharge. The work presented here involves running new undamaged bearings through to failure. This work primarily used accelerometers and acoustic emission sensors for trending and diagnostic capability. The results from new bearing tests are di!erent, because experiments using seeded defects have not o!ered insight into early detection of a naturally propagating crack. Most di$culties in detection of the early signals from a bearing defect are due to the mechanical noise in the wide spectrum of a bearing's vibration signal. Consequently, signal processing methods attempt to emphasise defect signals over background noise. The current state of bearing fault detection can be classi"ed into time-domain and frequencydomain techniques. These approaches primarily have used vibration and acoustic emission analysis. Two widely used techniques are the high-frequency resonance technique (HFRT) and the adaptive line enhancer (ALE). The ALE is used to decorrelate broadband noise from bearing signals by adjusting frequency "lter weights in proportion to the correlation of a delayed time-domain signal. Since narrowband signals are highly correlated and broadband noise is not, the "lter ampli"es signal and cancels noise. A recursive least mean-squares algorithm is used to adaptively calculate the "lter weights. The HFRT is a powerful tool that separates frequencies from vibrations generated by other mechanical elements. This technique has proven to be e!ective in monitoring bearings with incipient damage [1}6]. This paper addresses some of the aforementioned limitations by providing multiple sensor information before and during crack propagation. Time-domain techniques (root mean square (rms), Kurtosis and crest factor) are combined with the high-frequency 0888}3270/01/050979#15 $35.00/0
2001 Academic Press
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T. WILLIAMS E¹ A¸.
technique and ALE to detect and locate bearing damage. Both roller bearings and ball bearings were tested. Other sensors were also monitored, such as: housing and lubricant temperature, rotational speed and a magnetic plug for collecting metal debris.
2. DESCRIPTION OF EXPERIMENTAL APPARATUS
The set-up has three sub-systems: a test housing system, an oil circulation system and a sensors and data acquisition sub-system. The test housing system loads and spins four bearings. The oil circulation system regulates the #ow and temperature of the lubricant. These sensors and test equipment include multiple sensors, signal conditioning units and data acquisition hardware. Sensors monitor vibrations, elastic strain waves, bearing temperature, lubricant temperature and #ow, rotational speed and metal debris around bearings and oil lines. The test housing system incorporates a Timken test housing, internal tooling, a hydraulic loading mechanism and a drive mechanism. Two sets of tooling allow testing of tapered roller bearings, cylindrical roller bearings and ball bearings. Figure 1 shows a schematic of the test housing, donated by the Timken Company. Inside the 152.4 mm (6 in) bore of the test housing are three large pieces of hardened steel into which the outer races are pressed. The schematic shows oil inlet and outlet ports that lubricate each bearing. The tooling directs the oil #ow to the tested bearings 1}4. Additionally, the "gure illustrates the loading piston, the end-cap and oil passages. The loading piston transfers load from a hydraulic ram to the centre tooling. The end-cap regulates axial pre-load. This work concentrated on ball and cylindrical roller bearings. The type of ball bearing used in this research is a Torrington 208-K bearing. This type of bearing is a single-row deep-groove ball bearing of the Conrad type or non-"lling slot assembly. The static load rating, C , is 17.6 kN (4000 lbs), whereas the dynamic load, C, is 36.0 kN (8150 lbs). Testing Q conditions at 50% of the dynamic load rating result in a theoretical expected fatigue life, ¸ , of 9.2 hours or 3.3 million revolutions. Both Nachi and NSK NJ208 roller bearings were used. The static load rating is 25 kN (5600 lbs) and the dynamic load is 40 kN (9000 lbs).
Figure 1. Schematic of the test housing.
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Figure 2. Schematic of the oil circulation system.
The bearing system can be loaded axially and radially. However, radial loading applied through a hydraulic piston is the primary loading mechanism. A torque wrench tightens six -20 UNC cap-head bolts transferring an axial load of 9.24 kN (2077 lbf). Each bearing received loads between 50 and 60% of their dynamic load rating, resulting in a calculated cycle-life of 4.4 million revolutions. A 10 hp Baldor induction servo-motor with a series 18H pulse-width modulation controller. A Kistler 8792A50 triaxial K-shear accelerometer measures vibrations, and a general purpose AE sensor, Physical Acoustics R15, measures stress waves. Type J thermocouples (iron and copper}nickel) cover temperatures between 0 and 7503C (32}13823F). Four thermocouples press against the outer race of each bearing to record bearing temperatures. Also, outlet oil temperature and inlet oil temperatures are directly recorded to a data acquisition card. A magnetic contact probe, mounted next to the coupler, monitors shaft speed, and an Omega PX242-060G transducer measures the inlet oil pressure (Fig. 2). The system resonant frequencies fall between 5700 and 6000 Hz for each of the three directions measured with the accelerometer [5]. A band- pass window of 4500}6500 Hz was selected for the HFRT processing routine. The time-domain parameters were calculated from the raw signal as well as after band passing. Data from the thermocouples were collected continuously, at a sampling frequency of 5 Hz. Data from the accelerometer and acoustic emission sensors were acquired at intervals of 2}5 min. A total of 2 data points was taken per accelerometer axis as well as for the acoustic emission sensor. The sampling frequency was 30 kHz for each, for a record length of 2.18 s. 3. EXPERIMENT*ROLLER BEARINGS
The bearings were press "t into tooling of Graph MO 06 steel and onto a drive shaft of carburised AISI 4140 steel. The tooling contained positions for up to four bearings. One
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roller bearing was installed in an inner position for each test. Fafnir brand, type 208K ball bearings were used in the two outer positions to provide additional support for shaft. Three tests that resulted in damage to a roller bearing were run at a constant speed and load. The speed was set to 6000 rpm and the load to 67% of the dynamic rated load. An additional four tests also resulting in bearing failure were run under the same loading condition, but with varying speed. For these, the speed was changed at 15 min intervals in a cyclic pattern, from 3250}4000 to 5250}6000 rpm. With four speeds selected, a full cycle was completed once every hour.
4. RESULTS*ROLLER BEARINGS
The resulting damage generated on the roller bearings from the life tests is summarised in Table 1. For three experiments, spalling occurred on the inner race of the roller bearing. Three outer race failures were generated as well. One test resulted in a defect on a single rolling element. 4.1. TEMPERATURE AND DEBRIS TRENDS The bearing temperatures attained steady state during the tests run at constant speeds. There was no clear trend in temperature relative to bearing damage. The variable speed tests did illustrate the in#uence of speed on the bearing temperatures. Three hours of temperature data from test 4 is shown in Fig. 3. The temperature increase at minute 245 coincides with the speed increase from 3250 to 5250 rpm. The speed reduction to 4000 rpm at 260 min is accompanied by a decrease in temperature on the graph. The change to the maximum speed of 6000 rpm is marked by an increase at 275 min. The temperature peaks and begins to fall again with the completion of the speed cycle and the return to 3250 rpm at 290 min. During the last half hour of these tests, the temperature of the roller bearing did increase as spall damage developed and propagated on a single rolling element. This was the only test in which a change in temperature with bearing failure occurred. Since the magnetic plug samples were collected only once every 30 min and the damage propagation took place over a much shorter interval, there is no smooth increasing trend in the graphs of that data. The graphs of sample weight vs the normalised test times for each damage-producing test are shown in Figs 4 and 5. The greater "nal values for the inner race tests re#ect the much larger damage zones produced by that failure mode.
TABLE 1
¸ife test summary Test number
Speed (rpm)
Test duration Test duration (hour) (mil rev)
1 2 3
6000 6000 6000
2.42 (20.94) 16.98
4 5 6 7
Variable Variable Variable Variable
10.49 8.47 0.59 1.14
Failure mode
Damage size (mm)
0.871 (7.54) 6.113
Inner race Outer race Outer race
569 28 21
2.908 2.345 0.144 0.457
Roller Outer race Inner race Inner race
38 25 267 252
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Figure 3. Temperature trend for Test 4, without damage: **, T1; )))䉫))), T2; ***, T4.
Figure 4. Magnetic plug sample trend for inner race failures: } };} }, Test 1; 䉬, Test 6; - - -#- - -, Test 7.
5. ROLLER BEARING*TIME-DOMAIN TRENDS
5.1. rms The rms level from the accelerometer showed an increase for defects located in each of the three damage regions. The acoustic emission signal level proved to be sensitive to damage on the inner race, much less so to defects on rolling elements and unresponsive to outer race failures. It is theorised that this may be a result of impact with the shaft which can produce a larger elastic stress wave due to the larger inherent #exibility of the shaft in comparison to the outer race*which is pressed into a large massive steel component. The lower energy content in the bandpassed signal resulted in reduced rms values. However, the general trends in the rms data with and without bandpassing remained the same.
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Figure 5. Magnetic plug sample trend for outer race and roller failures: *䉫*, Test 2; } };} } , Test 3; } -䉬- }, Test 4; - - -#- - -, Test 5.
Figure 6. Test 1 rms trend with inner race damage: 䊏, x-axis; 䊊, y-axis, 䉫, z-axis.
The accelerometer signal level for Test 1, which resulted in extensive inner race damage, increased to a maximum level, then decreased and rose again. This #uctuating trend can be explained by the nature of the damage. The initial increase was caused by the appearance and initial propagation of the surface defect. The subsequent drop in signal level may be attributable to a phenomenon known as &healing'. The term applies to the smoothing of the sharp edges of a crack or small damage zone by continued rolling contact. There may have been a stall in the crack propagation during this period, or the decrease in vibration amplitude due to this smoothing may have overpowered the increase in signal level due to additional #aking. As the damage spread over a broader area, the signal rms rises again. The AE sensor signal exhibits a single increase, beginning about 10 min after the increase in accelerometer rms. At the end of this test, the damage zone spanned about 1/3 of the circumference of the inner race. The accelerometer data plot is shown in Fig. 6 and is typical of the tests.
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The variable speed tests illustrated the speed dependence of the rms value. Figure 7 shows a plot of speed vs rms from one test before the development of a bearing defect. There is an increasing trend in rms with speed for the accelerometer that is more marked for the z-axis. In contrast, the acoustic emission sensor showed a slight decreasing trend. 5.2. KURTOSIS Kurtosis values should be close to 3 for new bearings. The introduction of a defect onto any contacting surface would generate impulses, thus changing the distribution of the vibration signal and increasing the Kurtosis value. Once the damage area becomes greater than the spacing of the rolling elements, the continuous shock load (resulting from one decaying impulse running into the start of the next impulse) would bring the signal back to a normal distribution and return the Kurtosis value to 3. These trends were clear in the accelerometer signal from Test 1, which was run at a constant speed and generated extensive inner race damage. The constant speed tests, with ball or outer race defects, produced decreasing trends in the Kurtosis of the accelerometer signal. As can be seen from Table 2, the Kurtosis value appears to be more sensitive for the acoustic emission sensor than the rms parameter in detecting outer race failures. However, the Kurtosis level from the AE sensor for new bearings was consistently higher than 3. The variable speed tests show a slight speed dependence in the Kurtosis value for the accelerometer axis, and a more marked dependence for the signal from the acoustic emission sensor. Figure 8 shows that only the AE sensor exhibited such a large speed dependence.
Figure 7. Test 4 rms vs speed: *䊏*, x-axis; - -䊊- -, y-axis; - - -䉫- - -, z-axis; *#* -, ae.
TABLE 2
Deterministic frequencies Shaft speed (rpm) 3250 4000 5250 6000
Shaft frequency Roller defect Cage frequency (1;) (f) (f) P A 54.17 66.67 87.50 100.00
157.99 194.44 255.21 291.67
22.57 27.78 36.47 41.67
Outer race defect (f )
Inner race defect (f )
293.40 361.11 473.96 541.67
410.76 505.56 663.54 758.83
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Figure 8. Test 5 Kurtosis vs speed: *䊏*, x-axis; } }䉫} }, y-axis; - - -䉫- - -, z-axis; * -#- *, ae.
Figure 9. Test 5 crest factor vs speed: *䊏*, x-axis; } }䉫} }, y-axis; - - -䉫- - -, z-axis; * -#- *, ae.
5.3. CREST FACTOR The trends in the crest factor (Fig. 9) for the accelerometer signal loosely mirror those of the Kurtosis parameter. The crest factor values for the acoustic emission sensor are less distinct. As with the Kurtosis results, Test 4 reveals a decreasing trend in crest factor vs speed for the accelerometer signal, and the reverse trend for the AE signal. The increase in crest factor with speed persists for the AE sensor in Test 5 as well. A slight decreasing trend remains for the x-axis of the accelerometer also, though it is not repeated in the y- and z-axis. 5.4. FREQUENCY-DOMAIN TRENDS Table 3 gives the characteristic frequencies for the speeds at which the roller bearing tests were run. Since the equations are based on bearing geometry and speed alone, variations due to high loading and slipping are not considered. However, with only one exception, the
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TABLE 3
Maximum magnitudes of defect frequency components without A¸E Failure mode
Shaft speed at failure
1 2 3
Inner race Outer race Outer race
6000 6000 6000
4 5 6 7
Roller Outer race Inner race Inner race
4000 4000 4000 6000
Test number
Defect frequency present
x-axis No ALE
y-axis No ALE
z-axis No ALE
AE No ALE
Single speed tests 760 1.45E!02 2.22E!02 3.01E!02 5.76E!02 541 6.49E!03 2.54E!02 5.05E!02 1.18E!02 541 3.48E!03 2.38E!02 4.20E!02 4.95E!02 Variable speed tests 190 1.60E!03 361 5.05E!03 506 4.87E!04 760 2.10E!05
7.25E!03 1.19E!02 1.89E!04 1.50E!03
1.37E!02 2.69E!02 6.32E!03 2.89E!03
4.55E!02 1.22E!03 9.85E!03 2.97E!02
TABLE 4
Maximum magnitudes of defect frequency components with A¸E Failure mode
Shaft speed at failure
1 2 3
Inner race Outer race Outer race
6000 6000 6000
4 5 6 7
Roller Outer race Inner race Inner race
4000 4000 4000 6000
Test number
Defect frequency present
x-axis with ALE
y-axis with ALE
z-axis with ALE
AE with ALE
Single speed tests 760 4.75E!03 1.25E!02 2.52E!02 5.27E!02 541 4.49E!04 1.64E!02 4.85E!02 2.83E!03 541 6.40E!05 1.70E!02 3.84E!02 4.70E!02 Variable speed tests 190 5.01E!06 361 2.59E!04 506 2.96E!04 760 2.96E!04
7.33E!04 3.18E!03 1.41E!04 1.41E!04
4.39E!03 2.09E!02 1.70E!03 1.70E!03
4.21E!02 2.96E!03 2.32E!03 2.32E!03
characteristic frequencies detected during the tests were within 0.5% of the theoretical values. The larger variation occurred in the defect frequencies of the test involving a rolling element failure. The defect frequency for a roller bearing with spall damage on one roller was lower than the listed value by 2.5%. Tables 3 and 4 list the defect frequencies present in the vibration signal. The maximum values of the peaks are given, both with and without ALE "ltering. Previous research by Shirioshi and Li [4] involved the implementation of the ALE to detect bearing defects. Tapered roller bearings with arti"cially generated cracks were used. The work illustrated the e!ectiveness of the ALE in detecting early failures. It was noted that the non-linear transformation of the "ltration process nulli"ed the relation between absolute signal magnitude and physical damage level. However, in the past research with natural crack development in ball bearings, Ribadeneira [7] noted that the relative changes in the frequency peaks with ALE-processed data were relevant (1999). The signal magnitude change from the ALE "ltration of the data is evident from the values in the tables. Although the individual peaks are stronger without ALE processing, the reduction of signal noise makes the signal and the overall modulation pattern clearer after "ltration. All of the spectra were from demodulated signals.
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TABLE 5
Signal trend summary (2f denotes second harmonic) Accelerometer (z-axis)/acoustic emission sensor Test Failure mode number
rms
Kurtosis
Crest
Defect
Shaft
Cage
Dominant
Inner race
1 6 7
! /! !/! !/!
! /! !/! !/
/ !/! !/
#/# #/# #/#
#/# #/# #/#
#/# !/! !/!
Shaft Shaft Shaft
Outer race
2 3 5
!/! !/! !/!
/! /! !/!
/! /! /!
#/# #/# #/!
#/# #/# #/#
!/! !/! !/!
Defect Defect Defect
Roller
4
!/!
/!
!/!
#(2-)/#
#/#
#/#
Cage/defect
Table 5 gives an overview of the trends in the vibration and acoustic emission signals according to the failure mode. For the time-domain parameters, the arrows indicate an increasing or decreasing trend in the value of the parameter as the damage develops and propagates. The characteristic frequencies that are present in the vibration signal after damage has developed are indicated as well. A plus sign indicates the presence of the signal and a minus sign indicates its absence. As evident from Table 5, the spectrum of the vibration signal for these tests of an inner-race failure was dominated by the fundamental frequency. Strong harmonics were present as well. The inner race defect frequency was heavily modulated by the shaft frequency, producing a series of sidebands. In contrast, the outer race failures produced signals that are dominated by the defect frequencies. Also in Table 5, it is shown that the acoustic emission sensor is unresponsive to outer race failures. However, though the outer race defect frequency is absent from Test 5, it did appear for Tests 2 and 3.
6. EXPERIMENTS*BALL BEARINGS
At the beginning of the program, ball bearings were tested in a run-to-failure condition. According to Braun and Datner [8], once a crack is seeded, propagation occurs rapidly. A by-product of this methodology was an ambiguous de"nition of failure, and early tests produced catastrophic failures that damaged the test set-up. The authors re"ned the procedures so that any signi"cant change in accelerometer, acoustic emission or magnetic plug data halted the test. Therefore, the lifetimes shown in these data is near the threshold of early detection (a clear presence of defect frequencies), but also not far from catastrophic failure (2}4 times the baseline rms as shown in Fig. 6). Life-tests of 208 K ball bearings in mineral oil showed premature failures over bearings tested in synthetic oil with similar properties. Currently, diagnostic technologies do not signal break-in failures such as improper assembly, high temperatures, poor lubrication and inappropriate loading. Figure 10 summarizes life-test experiments of 208K ball bearings in synthetic oil with an average bearing life of 13.4 hours at 6000 rpm and 50}60% of the dynamic rated load. Figure 11 shows the percentages of inner and outer race damages. Experimental results show that inner raceways are more prone to failure than outer raceways or rolling elements. For this "gure, two possible explanation are o!ered: (1) The rotational speed of the cage
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Figure 10. Summary of life bearing tests in synthetic oil.
Figure 11. Inner and outer race failures in ball bearings.
relative to the inner race is higher than that relative to the outer race. This kinematic relation translates to a ball pass inner race frequency being about 29% higher than the ball pass outer race frequency. (2) Contact forces in the inner race concentrate in smaller areas due to a smaller radius of curvature, resulting in higher stresses. 7. SPECTRUM ANALYSIS*BALL BEARINGS
As seen in Figure 12, noise corrupts the enveloped spectrum resulting from HFRT. In particular, noise camou#ages frequency components with small amplitudes such as cage or rolling element frequencies. Fortunately, ALE e!ectively enhances the frequency spectrum of ball bearings (Fig. 15). The trade o! from using the ALE is the non-linear scaling of peak magnitudes (due to the nature of the "lters used in ALE); however, in practice relative changes between peaks are relevant, not absolute magnitudes. Speci"cally, rms and Kurtosis combine with the HFRT [9}13] and ALE to diagnose bearing damage. HFRT and ALE have proven to be e!ective in monitoring bearing condition with incipient damages. Figure 13 presents the envelope spectrum for new bearings. There is essentially no signal in the envelope spectrum save an artifact arising from the 60 Hz line frequency. Figure 14
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Figure 12. Envelope spectrum, outer race damage, without ALE.
Figure 13. Enveloped spectrum for four new bearings.
shows the spectrum towards the end of the tests. The axial component of the accelerometer signal and the AE sensor clearly identify the rotational frequency of 100 Hz modulating the cage frequency. Distinct peaks are at 100, 145, 245, 445 and 545 Hz in the axial direction and AE spectrum. The analysis of these plots reveals that inner raceway damage is easily
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Figure 14. Envelope spectrum, inner race damage.
Figure 15. Envelope spectrum, outer race damage.
detected by the cage frequency of 41.7 Hz modulated by the rotational frequency of 100 Hz (with worn bearings there is a variation with cage position as well as the rotation of the defect through the load zone). Even though elastic waves need to travel from the inner race to the housing; this modulation strongly appears on the AE sensor.
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Figure 15 shows a spectrum from bearing with a defect in the outer race. The AE sensor was not able to detect the defect. The outer race failures also did not typically show a large peak at the rotational frequency, as is the case for inner race defects.
8. CONCLUSIONS
The contribution of this work is the implementation of life and testing capabilities. The new system is capable of providing &real' crack information. It is believed that the research is the "rst one to combine multiple sensors to monitor bearing condition without an arti"cially induced crack. This approach, recording data throughout the bearing life, is unique because it di!ers from other studies that are limited to arti"cially induced damages. The data collected throughout life-tests constitutes the foundation for a growing expertknowledge database. The sensors used in this study include thermocouples, a pressure #ow transducer, a magnetic contact probe, wear site sensors, an accelerometer and acoustic emission sensors. It also provides information related to natural crack growth in later stages of damage.
ACKNOWLEDGEMENTS This work was partially funded by the O$ce of Naval Research under research grant number N00014-95-10539, entitled &Integrated Diagnostics.' The other partner is ExperTech Inc. Any opinions, "ndings and conclusions or recommendations are those of the author and do not necessarily re#ect the views of O$ce of Naval Research, ExperTech Inc. or The Georgia Institute of Technology.
REFERENCES 1. P. D. MCFADDEN and J. D. SMITH 1984 International Journal of ¹ribology 17, 1}18. Vibration monitoring of rolling element bearings by the high frequency resonance technique*a review. 2. R. J. ALFREDSON and J. MATHEW 1985 ¹he Institute of Engineers, Australia, Mechanical Engineering ¹ransactions 10, 102}107. Time domain methods for monitoring the condition of rolling element bearings. 3. R. J. ALFREDSON and J. MATHEW 1985 ¹he Institute of Engineers, Australia, Mechanical Engineering ¹ransactions 10, 108}112. Frequency domain methods for monitoring the condition of rolling element bearings. 4. Y. LI, J. SHIROISHI, S. DANYLUK, T. KURFESS and S. Y. LIANG 1997 21st Biennial Conference on Reliability, Stress Analysis and Failure Prevention (RSAFP),
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11. M. R. HOEPRICH 1992, ¹ransactions of ASME, Journal of ¹ribology 114, 328}333. Rolling element bearing fatigue damage propagation. 12. P. D. MCFADDEN and J. D. SMITH 1985 Journal of Sound and
ROLLING ELEMENT BEARING DIAGNOSTICS IN RUN-TO-FAILURE LIFETIME TESTING T. WILLIAMS, X. RIBADENEIRA, S. BILLINGTON
AND
T. KURFESS
George W. Woodruw School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, U.S.A. Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failures can be catastrophic and can result in costly downtime. To date, most research has studied crack propagation resulting from arti"cial or &seeded' damage. This damage has been induced in bearings by: scratching the surface, introducing debris into the lubricant or machined with an electrical discharge. The work presented here involves running new undamaged ball and roller bearings through to failure. Traditional vibration metrics such as: root mean square, peak value, kurtosis and crest factor are recorded through the test duration from accelerometers and acoustic emission sensors. 2001 Academic Press
1. INTRODUCTION AND MOTIVATION
Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failures can be catastrophic and can result in costly downtime. Therefore, bearing monitoring poses important challenges to machine maintenance and automation processes. In fact, the importance of bearing condition has resulted in four decades of research on the causes of bearing failure. To date, most research has studied crack propagation resulting from arti"cial or &seeded' damage. This damage has been induced by: scratching the surface, introducing debris into the lubricant or machined with an electrical discharge. The work presented here involves running new undamaged bearings through to failure. This work primarily used accelerometers and acoustic emission sensors for trending and diagnostic capability. The results from new bearing tests are di!erent, because experiments using seeded defects have not o!ered insight into early detection of a naturally propagating crack. Most di$culties in detection of the early signals from a bearing defect are due to the mechanical noise in the wide spectrum of a bearing's vibration signal. Consequently, signal processing methods attempt to emphasise defect signals over background noise. The current state of bearing fault detection can be classi"ed into time-domain and frequencydomain techniques. These approaches primarily have used vibration and acoustic emission analysis. Two widely used techniques are the high-frequency resonance technique (HFRT) and the adaptive line enhancer (ALE). The ALE is used to decorrelate broadband noise from bearing signals by adjusting frequency "lter weights in proportion to the correlation of a delayed time-domain signal. Since narrowband signals are highly correlated and broadband noise is not, the "lter ampli"es signal and cancels noise. A recursive least mean-squares algorithm is used to adaptively calculate the "lter weights. The HFRT is a powerful tool that separates frequencies from vibrations generated by other mechanical elements. This technique has proven to be e!ective in monitoring bearings with incipient damage [1}6]. This paper addresses some of the aforementioned limitations by providing multiple sensor information before and during crack propagation. Time-domain techniques (root mean square (rms), Kurtosis and crest factor) are combined with the high-frequency 0888}3270/01/050979#15 $35.00/0
2001 Academic Press
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T. WILLIAMS E¹ A¸.
technique and ALE to detect and locate bearing damage. Both roller bearings and ball bearings were tested. Other sensors were also monitored, such as: housing and lubricant temperature, rotational speed and a magnetic plug for collecting metal debris.
2. DESCRIPTION OF EXPERIMENTAL APPARATUS
The set-up has three sub-systems: a test housing system, an oil circulation system and a sensors and data acquisition sub-system. The test housing system loads and spins four bearings. The oil circulation system regulates the #ow and temperature of the lubricant. These sensors and test equipment include multiple sensors, signal conditioning units and data acquisition hardware. Sensors monitor vibrations, elastic strain waves, bearing temperature, lubricant temperature and #ow, rotational speed and metal debris around bearings and oil lines. The test housing system incorporates a Timken test housing, internal tooling, a hydraulic loading mechanism and a drive mechanism. Two sets of tooling allow testing of tapered roller bearings, cylindrical roller bearings and ball bearings. Figure 1 shows a schematic of the test housing, donated by the Timken Company. Inside the 152.4 mm (6 in) bore of the test housing are three large pieces of hardened steel into which the outer races are pressed. The schematic shows oil inlet and outlet ports that lubricate each bearing. The tooling directs the oil #ow to the tested bearings 1}4. Additionally, the "gure illustrates the loading piston, the end-cap and oil passages. The loading piston transfers load from a hydraulic ram to the centre tooling. The end-cap regulates axial pre-load. This work concentrated on ball and cylindrical roller bearings. The type of ball bearing used in this research is a Torrington 208-K bearing. This type of bearing is a single-row deep-groove ball bearing of the Conrad type or non-"lling slot assembly. The static load rating, C , is 17.6 kN (4000 lbs), whereas the dynamic load, C, is 36.0 kN (8150 lbs). Testing Q conditions at 50% of the dynamic load rating result in a theoretical expected fatigue life, ¸ , of 9.2 hours or 3.3 million revolutions. Both Nachi and NSK NJ208 roller bearings were used. The static load rating is 25 kN (5600 lbs) and the dynamic load is 40 kN (9000 lbs).
Figure 1. Schematic of the test housing.
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Figure 2. Schematic of the oil circulation system.
The bearing system can be loaded axially and radially. However, radial loading applied through a hydraulic piston is the primary loading mechanism. A torque wrench tightens six -20 UNC cap-head bolts transferring an axial load of 9.24 kN (2077 lbf). Each bearing received loads between 50 and 60% of their dynamic load rating, resulting in a calculated cycle-life of 4.4 million revolutions. A 10 hp Baldor induction servo-motor with a series 18H pulse-width modulation controller. A Kistler 8792A50 triaxial K-shear accelerometer measures vibrations, and a general purpose AE sensor, Physical Acoustics R15, measures stress waves. Type J thermocouples (iron and copper}nickel) cover temperatures between 0 and 7503C (32}13823F). Four thermocouples press against the outer race of each bearing to record bearing temperatures. Also, outlet oil temperature and inlet oil temperatures are directly recorded to a data acquisition card. A magnetic contact probe, mounted next to the coupler, monitors shaft speed, and an Omega PX242-060G transducer measures the inlet oil pressure (Fig. 2). The system resonant frequencies fall between 5700 and 6000 Hz for each of the three directions measured with the accelerometer [5]. A band- pass window of 4500}6500 Hz was selected for the HFRT processing routine. The time-domain parameters were calculated from the raw signal as well as after band passing. Data from the thermocouples were collected continuously, at a sampling frequency of 5 Hz. Data from the accelerometer and acoustic emission sensors were acquired at intervals of 2}5 min. A total of 2 data points was taken per accelerometer axis as well as for the acoustic emission sensor. The sampling frequency was 30 kHz for each, for a record length of 2.18 s. 3. EXPERIMENT*ROLLER BEARINGS
The bearings were press "t into tooling of Graph MO 06 steel and onto a drive shaft of carburised AISI 4140 steel. The tooling contained positions for up to four bearings. One
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roller bearing was installed in an inner position for each test. Fafnir brand, type 208K ball bearings were used in the two outer positions to provide additional support for shaft. Three tests that resulted in damage to a roller bearing were run at a constant speed and load. The speed was set to 6000 rpm and the load to 67% of the dynamic rated load. An additional four tests also resulting in bearing failure were run under the same loading condition, but with varying speed. For these, the speed was changed at 15 min intervals in a cyclic pattern, from 3250}4000 to 5250}6000 rpm. With four speeds selected, a full cycle was completed once every hour.
4. RESULTS*ROLLER BEARINGS
The resulting damage generated on the roller bearings from the life tests is summarised in Table 1. For three experiments, spalling occurred on the inner race of the roller bearing. Three outer race failures were generated as well. One test resulted in a defect on a single rolling element. 4.1. TEMPERATURE AND DEBRIS TRENDS The bearing temperatures attained steady state during the tests run at constant speeds. There was no clear trend in temperature relative to bearing damage. The variable speed tests did illustrate the in#uence of speed on the bearing temperatures. Three hours of temperature data from test 4 is shown in Fig. 3. The temperature increase at minute 245 coincides with the speed increase from 3250 to 5250 rpm. The speed reduction to 4000 rpm at 260 min is accompanied by a decrease in temperature on the graph. The change to the maximum speed of 6000 rpm is marked by an increase at 275 min. The temperature peaks and begins to fall again with the completion of the speed cycle and the return to 3250 rpm at 290 min. During the last half hour of these tests, the temperature of the roller bearing did increase as spall damage developed and propagated on a single rolling element. This was the only test in which a change in temperature with bearing failure occurred. Since the magnetic plug samples were collected only once every 30 min and the damage propagation took place over a much shorter interval, there is no smooth increasing trend in the graphs of that data. The graphs of sample weight vs the normalised test times for each damage-producing test are shown in Figs 4 and 5. The greater "nal values for the inner race tests re#ect the much larger damage zones produced by that failure mode.
TABLE 1
¸ife test summary Test number
Speed (rpm)
Test duration Test duration (hour) (mil rev)
1 2 3
6000 6000 6000
2.42 (20.94) 16.98
4 5 6 7
Variable Variable Variable Variable
10.49 8.47 0.59 1.14
Failure mode
Damage size (mm)
0.871 (7.54) 6.113
Inner race Outer race Outer race
569 28 21
2.908 2.345 0.144 0.457
Roller Outer race Inner race Inner race
38 25 267 252
ROLLING ELEMENT BEARING DIAGNOSTICS
983
Figure 3. Temperature trend for Test 4, without damage: **, T1; )))䉫))), T2; ***, T4.
Figure 4. Magnetic plug sample trend for inner race failures: } };} }, Test 1; 䉬, Test 6; - - -#- - -, Test 7.
5. ROLLER BEARING*TIME-DOMAIN TRENDS
5.1. rms The rms level from the accelerometer showed an increase for defects located in each of the three damage regions. The acoustic emission signal level proved to be sensitive to damage on the inner race, much less so to defects on rolling elements and unresponsive to outer race failures. It is theorised that this may be a result of impact with the shaft which can produce a larger elastic stress wave due to the larger inherent #exibility of the shaft in comparison to the outer race*which is pressed into a large massive steel component. The lower energy content in the bandpassed signal resulted in reduced rms values. However, the general trends in the rms data with and without bandpassing remained the same.
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Figure 5. Magnetic plug sample trend for outer race and roller failures: *䉫*, Test 2; } };} } , Test 3; } -䉬- }, Test 4; - - -#- - -, Test 5.
Figure 6. Test 1 rms trend with inner race damage: 䊏, x-axis; 䊊, y-axis, 䉫, z-axis.
The accelerometer signal level for Test 1, which resulted in extensive inner race damage, increased to a maximum level, then decreased and rose again. This #uctuating trend can be explained by the nature of the damage. The initial increase was caused by the appearance and initial propagation of the surface defect. The subsequent drop in signal level may be attributable to a phenomenon known as &healing'. The term applies to the smoothing of the sharp edges of a crack or small damage zone by continued rolling contact. There may have been a stall in the crack propagation during this period, or the decrease in vibration amplitude due to this smoothing may have overpowered the increase in signal level due to additional #aking. As the damage spread over a broader area, the signal rms rises again. The AE sensor signal exhibits a single increase, beginning about 10 min after the increase in accelerometer rms. At the end of this test, the damage zone spanned about 1/3 of the circumference of the inner race. The accelerometer data plot is shown in Fig. 6 and is typical of the tests.
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The variable speed tests illustrated the speed dependence of the rms value. Figure 7 shows a plot of speed vs rms from one test before the development of a bearing defect. There is an increasing trend in rms with speed for the accelerometer that is more marked for the z-axis. In contrast, the acoustic emission sensor showed a slight decreasing trend. 5.2. KURTOSIS Kurtosis values should be close to 3 for new bearings. The introduction of a defect onto any contacting surface would generate impulses, thus changing the distribution of the vibration signal and increasing the Kurtosis value. Once the damage area becomes greater than the spacing of the rolling elements, the continuous shock load (resulting from one decaying impulse running into the start of the next impulse) would bring the signal back to a normal distribution and return the Kurtosis value to 3. These trends were clear in the accelerometer signal from Test 1, which was run at a constant speed and generated extensive inner race damage. The constant speed tests, with ball or outer race defects, produced decreasing trends in the Kurtosis of the accelerometer signal. As can be seen from Table 2, the Kurtosis value appears to be more sensitive for the acoustic emission sensor than the rms parameter in detecting outer race failures. However, the Kurtosis level from the AE sensor for new bearings was consistently higher than 3. The variable speed tests show a slight speed dependence in the Kurtosis value for the accelerometer axis, and a more marked dependence for the signal from the acoustic emission sensor. Figure 8 shows that only the AE sensor exhibited such a large speed dependence.
Figure 7. Test 4 rms vs speed: *䊏*, x-axis; - -䊊- -, y-axis; - - -䉫- - -, z-axis; *#* -, ae.
TABLE 2
Deterministic frequencies Shaft speed (rpm) 3250 4000 5250 6000
Shaft frequency Roller defect Cage frequency (1;) (f) (f) P A 54.17 66.67 87.50 100.00
157.99 194.44 255.21 291.67
22.57 27.78 36.47 41.67
Outer race defect (f )
Inner race defect (f )
293.40 361.11 473.96 541.67
410.76 505.56 663.54 758.83
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Figure 8. Test 5 Kurtosis vs speed: *䊏*, x-axis; } }䉫} }, y-axis; - - -䉫- - -, z-axis; * -#- *, ae.
Figure 9. Test 5 crest factor vs speed: *䊏*, x-axis; } }䉫} }, y-axis; - - -䉫- - -, z-axis; * -#- *, ae.
5.3. CREST FACTOR The trends in the crest factor (Fig. 9) for the accelerometer signal loosely mirror those of the Kurtosis parameter. The crest factor values for the acoustic emission sensor are less distinct. As with the Kurtosis results, Test 4 reveals a decreasing trend in crest factor vs speed for the accelerometer signal, and the reverse trend for the AE signal. The increase in crest factor with speed persists for the AE sensor in Test 5 as well. A slight decreasing trend remains for the x-axis of the accelerometer also, though it is not repeated in the y- and z-axis. 5.4. FREQUENCY-DOMAIN TRENDS Table 3 gives the characteristic frequencies for the speeds at which the roller bearing tests were run. Since the equations are based on bearing geometry and speed alone, variations due to high loading and slipping are not considered. However, with only one exception, the
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ROLLING ELEMENT BEARING DIAGNOSTICS
TABLE 3
Maximum magnitudes of defect frequency components without A¸E Failure mode
Shaft speed at failure
1 2 3
Inner race Outer race Outer race
6000 6000 6000
4 5 6 7
Roller Outer race Inner race Inner race
4000 4000 4000 6000
Test number
Defect frequency present
x-axis No ALE
y-axis No ALE
z-axis No ALE
AE No ALE
Single speed tests 760 1.45E!02 2.22E!02 3.01E!02 5.76E!02 541 6.49E!03 2.54E!02 5.05E!02 1.18E!02 541 3.48E!03 2.38E!02 4.20E!02 4.95E!02 Variable speed tests 190 1.60E!03 361 5.05E!03 506 4.87E!04 760 2.10E!05
7.25E!03 1.19E!02 1.89E!04 1.50E!03
1.37E!02 2.69E!02 6.32E!03 2.89E!03
4.55E!02 1.22E!03 9.85E!03 2.97E!02
TABLE 4
Maximum magnitudes of defect frequency components with A¸E Failure mode
Shaft speed at failure
1 2 3
Inner race Outer race Outer race
6000 6000 6000
4 5 6 7
Roller Outer race Inner race Inner race
4000 4000 4000 6000
Test number
Defect frequency present
x-axis with ALE
y-axis with ALE
z-axis with ALE
AE with ALE
Single speed tests 760 4.75E!03 1.25E!02 2.52E!02 5.27E!02 541 4.49E!04 1.64E!02 4.85E!02 2.83E!03 541 6.40E!05 1.70E!02 3.84E!02 4.70E!02 Variable speed tests 190 5.01E!06 361 2.59E!04 506 2.96E!04 760 2.96E!04
7.33E!04 3.18E!03 1.41E!04 1.41E!04
4.39E!03 2.09E!02 1.70E!03 1.70E!03
4.21E!02 2.96E!03 2.32E!03 2.32E!03
characteristic frequencies detected during the tests were within 0.5% of the theoretical values. The larger variation occurred in the defect frequencies of the test involving a rolling element failure. The defect frequency for a roller bearing with spall damage on one roller was lower than the listed value by 2.5%. Tables 3 and 4 list the defect frequencies present in the vibration signal. The maximum values of the peaks are given, both with and without ALE "ltering. Previous research by Shirioshi and Li [4] involved the implementation of the ALE to detect bearing defects. Tapered roller bearings with arti"cially generated cracks were used. The work illustrated the e!ectiveness of the ALE in detecting early failures. It was noted that the non-linear transformation of the "ltration process nulli"ed the relation between absolute signal magnitude and physical damage level. However, in the past research with natural crack development in ball bearings, Ribadeneira [7] noted that the relative changes in the frequency peaks with ALE-processed data were relevant (1999). The signal magnitude change from the ALE "ltration of the data is evident from the values in the tables. Although the individual peaks are stronger without ALE processing, the reduction of signal noise makes the signal and the overall modulation pattern clearer after "ltration. All of the spectra were from demodulated signals.
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T. WILLIAMS E¹ A¸.
TABLE 5
Signal trend summary (2f denotes second harmonic) Accelerometer (z-axis)/acoustic emission sensor Test Failure mode number
rms
Kurtosis
Crest
Defect
Shaft
Cage
Dominant
Inner race
1 6 7
! /! !/! !/!
! /! !/! !/
/ !/! !/
#/# #/# #/#
#/# #/# #/#
#/# !/! !/!
Shaft Shaft Shaft
Outer race
2 3 5
!/! !/! !/!
/! /! !/!
/! /! /!
#/# #/# #/!
#/# #/# #/#
!/! !/! !/!
Defect Defect Defect
Roller
4
!/!
/!
!/!
#(2-)/#
#/#
#/#
Cage/defect
Table 5 gives an overview of the trends in the vibration and acoustic emission signals according to the failure mode. For the time-domain parameters, the arrows indicate an increasing or decreasing trend in the value of the parameter as the damage develops and propagates. The characteristic frequencies that are present in the vibration signal after damage has developed are indicated as well. A plus sign indicates the presence of the signal and a minus sign indicates its absence. As evident from Table 5, the spectrum of the vibration signal for these tests of an inner-race failure was dominated by the fundamental frequency. Strong harmonics were present as well. The inner race defect frequency was heavily modulated by the shaft frequency, producing a series of sidebands. In contrast, the outer race failures produced signals that are dominated by the defect frequencies. Also in Table 5, it is shown that the acoustic emission sensor is unresponsive to outer race failures. However, though the outer race defect frequency is absent from Test 5, it did appear for Tests 2 and 3.
6. EXPERIMENTS*BALL BEARINGS
At the beginning of the program, ball bearings were tested in a run-to-failure condition. According to Braun and Datner [8], once a crack is seeded, propagation occurs rapidly. A by-product of this methodology was an ambiguous de"nition of failure, and early tests produced catastrophic failures that damaged the test set-up. The authors re"ned the procedures so that any signi"cant change in accelerometer, acoustic emission or magnetic plug data halted the test. Therefore, the lifetimes shown in these data is near the threshold of early detection (a clear presence of defect frequencies), but also not far from catastrophic failure (2}4 times the baseline rms as shown in Fig. 6). Life-tests of 208 K ball bearings in mineral oil showed premature failures over bearings tested in synthetic oil with similar properties. Currently, diagnostic technologies do not signal break-in failures such as improper assembly, high temperatures, poor lubrication and inappropriate loading. Figure 10 summarizes life-test experiments of 208K ball bearings in synthetic oil with an average bearing life of 13.4 hours at 6000 rpm and 50}60% of the dynamic rated load. Figure 11 shows the percentages of inner and outer race damages. Experimental results show that inner raceways are more prone to failure than outer raceways or rolling elements. For this "gure, two possible explanation are o!ered: (1) The rotational speed of the cage
ROLLING ELEMENT BEARING DIAGNOSTICS
989
Figure 10. Summary of life bearing tests in synthetic oil.
Figure 11. Inner and outer race failures in ball bearings.
relative to the inner race is higher than that relative to the outer race. This kinematic relation translates to a ball pass inner race frequency being about 29% higher than the ball pass outer race frequency. (2) Contact forces in the inner race concentrate in smaller areas due to a smaller radius of curvature, resulting in higher stresses. 7. SPECTRUM ANALYSIS*BALL BEARINGS
As seen in Figure 12, noise corrupts the enveloped spectrum resulting from HFRT. In particular, noise camou#ages frequency components with small amplitudes such as cage or rolling element frequencies. Fortunately, ALE e!ectively enhances the frequency spectrum of ball bearings (Fig. 15). The trade o! from using the ALE is the non-linear scaling of peak magnitudes (due to the nature of the "lters used in ALE); however, in practice relative changes between peaks are relevant, not absolute magnitudes. Speci"cally, rms and Kurtosis combine with the HFRT [9}13] and ALE to diagnose bearing damage. HFRT and ALE have proven to be e!ective in monitoring bearing condition with incipient damages. Figure 13 presents the envelope spectrum for new bearings. There is essentially no signal in the envelope spectrum save an artifact arising from the 60 Hz line frequency. Figure 14
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T. WILLIAMS E¹ A¸.
Figure 12. Envelope spectrum, outer race damage, without ALE.
Figure 13. Enveloped spectrum for four new bearings.
shows the spectrum towards the end of the tests. The axial component of the accelerometer signal and the AE sensor clearly identify the rotational frequency of 100 Hz modulating the cage frequency. Distinct peaks are at 100, 145, 245, 445 and 545 Hz in the axial direction and AE spectrum. The analysis of these plots reveals that inner raceway damage is easily
ROLLING ELEMENT BEARING DIAGNOSTICS
991
Figure 14. Envelope spectrum, inner race damage.
Figure 15. Envelope spectrum, outer race damage.
detected by the cage frequency of 41.7 Hz modulated by the rotational frequency of 100 Hz (with worn bearings there is a variation with cage position as well as the rotation of the defect through the load zone). Even though elastic waves need to travel from the inner race to the housing; this modulation strongly appears on the AE sensor.
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T. WILLIAMS E¹ A¸.
Figure 15 shows a spectrum from bearing with a defect in the outer race. The AE sensor was not able to detect the defect. The outer race failures also did not typically show a large peak at the rotational frequency, as is the case for inner race defects.
8. CONCLUSIONS
The contribution of this work is the implementation of life and testing capabilities. The new system is capable of providing &real' crack information. It is believed that the research is the "rst one to combine multiple sensors to monitor bearing condition without an arti"cially induced crack. This approach, recording data throughout the bearing life, is unique because it di!ers from other studies that are limited to arti"cially induced damages. The data collected throughout life-tests constitutes the foundation for a growing expertknowledge database. The sensors used in this study include thermocouples, a pressure #ow transducer, a magnetic contact probe, wear site sensors, an accelerometer and acoustic emission sensors. It also provides information related to natural crack growth in later stages of damage.
ACKNOWLEDGEMENTS This work was partially funded by the O$ce of Naval Research under research grant number N00014-95-10539, entitled &Integrated Diagnostics.' The other partner is ExperTech Inc. Any opinions, "ndings and conclusions or recommendations are those of the author and do not necessarily re#ect the views of O$ce of Naval Research, ExperTech Inc. or The Georgia Institute of Technology.
REFERENCES 1. P. D. MCFADDEN and J. D. SMITH 1984 International Journal of ¹ribology 17, 1}18. Vibration monitoring of rolling element bearings by the high frequency resonance technique*a review. 2. R. J. ALFREDSON and J. MATHEW 1985 ¹he Institute of Engineers, Australia, Mechanical Engineering ¹ransactions 10, 102}107. Time domain methods for monitoring the condition of rolling element bearings. 3. R. J. ALFREDSON and J. MATHEW 1985 ¹he Institute of Engineers, Australia, Mechanical Engineering ¹ransactions 10, 108}112. Frequency domain methods for monitoring the condition of rolling element bearings. 4. Y. LI, J. SHIROISHI, S. DANYLUK, T. KURFESS and S. Y. LIANG 1997 21st Biennial Conference on Reliability, Stress Analysis and Failure Prevention (RSAFP),
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11. M. R. HOEPRICH 1992, ¹ransactions of ASME, Journal of ¹ribology 114, 328}333. Rolling element bearing fatigue damage propagation. 12. P. D. MCFADDEN and J. D. SMITH 1985 Journal of Sound and