Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Gary Kreider and Thomas Fichter

The Timken Company, North Canton, Ohio

Investigation of Tapered Roller Bearing

Damage Detection Using Oil Debris Analysis

NASA/TM—2006-214082

February 2006
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Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Gary Kreider and Thomas Fichter

The Timken Company, North Canton, Ohio

Investigation of Tapered Roller Bearing

Damage Detection Using Oil Debris Analysis

NASA/TM—2006-214082

February 2006

National Aeronautics and

Space Administration

Glenn Research Center

Prepared for the

2006 Aerospace Conference

sponsored by the Institute of Electrical and Electronics Engineers

Big Sky, Montana, March 4–11, 2006
Acknowledgments

The authors acknowledge the contributions of Dennis Shaughnessy, Pratt & Whitney Advanced Engine

Systems, for his leadership in project management and engineering support. The authors also thank

Mary J. Long Davis and Isaac Lopez, NASA Glenn Vehicle Systems Project Office,

for their dedicated support in program management of this research effort.

Available from

NASA Center for Aerospace Information

7121 Standard Drive

Hanover, MD 21076

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22100

Available electronically at http://gltrs.grc.nasa.gov

This work was sponsored by the Fundamental Aeronautics

Program at the NASA Glenn Research Center.
NASA/TM—2006-214082 1

Investigation of Tapered Roller Bearing Damage 

Detection Using Oil Debris Analysis 

Paula J. Dempsey 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Gary Kreider and Thomas Fichter 

The Timken Company 

North Canton, Ohio 44720 

Abstract 

A diagnostic tool was developed for detecting fatigue 

damage to tapered roller bearings. Tapered roller bearings 

are used in helicopter transmissions and have potential for 

use in high bypass advanced gas turbine aircraft engines. 

This diagnostic tool was developed and evaluated 

experimentally by collecting oil debris data from failure 

progression tests performed by The Timken Company in 

their Tapered Roller Bearing Health Monitoring Test Rig. 

Failure progression tests were performed under simulated 

engine load conditions. Tests were performed on one 

healthy bearing and three predamaged bearings. During 

each test, data from an on-line, in-line, inductance type oil 

debris sensor was monitored and recorded for the 

occurrence of debris generated during failure of the bearing. 

The bearing was removed periodically for inspection 

throughout the failure progression tests. Results indicate the 

accumulated oil debris mass is a good predictor of damage 

on tapered roller bearings. The use of a fuzzy logic model to 

enable an easily interpreted diagnostic metric was proposed 

and demonstrated. 

Introduction 

Oil analysis is one diagnostic tool used to indicate 

transmission and turbine engine health (ref. 1). Bearing 

fatigue failures in helicopter transmissions and aircraft 

turbine engines generate significant debris in their 

lubrication systems. Oil debris monitoring for detecting 

damaged bearings in engines and transmissions consists of 

periodic off-line analysis of oil samples, magnetic chip 

detectors that capture the ferrous particles from the oil line 

and inductance type on-line oil debris sensors that monitor 

disturbance to a magnetic field as debris particles pass 

through the sensor. 

Reviewing data from oil debris sensors often requires 

expert analysis. False alarms of oil debris technologies can 

be caused by non-failure debris. This debris can bridge the 

gap of plug type chip detectors. Inductance type oil debris 

sensors cannot differentiate between debris generated by a 

failing component and debris due to operational conditions 

(ref. 2). 

Monitoring debris in lubrication systems with an 

inductance type oil debris sensor has been successfully used 

to indicate damage to critical components in aircraft engines 

(ref. 3). Several companies manufacture inductance type oil 

debris sensors that measure debris size and count particles

(ref. 4). The oil debris sensor selected for this research was 

used in a previous analysis, and results demonstrated the 

debris mass measured by the sensor showed a significant 

increase when gear and bearing pitting damage began to 

occur (refs. 5 to 8). Earlier work performed at Timken also 

showed that particles measured by the inductance oil debris 

monitoring system (ODM) could provide early indication of 

impending failures during tapered roller bearing failure 

progression rig testing (refs. 9 and 10). Additional failure 

progression testing is required to define warning levels 

using oil debris analysis. 

The objective of the work reported herein is to first 

identify the best feature for detecting tapered roller bearing 

damage from a commercially available in-line, real-time, 

inductance type oil debris sensor. Once a feature for 

detecting bearing damage is defined through analysis of the 

oil debris data collected during failure progressions tests, a 

method for setting threshold limits for varying magnitudes 

of damage to the bearings will be identified. 

Test Facility Description 

Failure progression tests were conducted in the Tapered 

Roller Bearing Health Monitoring Test Rig at Timken. The 

test rig is illustrated in figure 1. The test rig consists of two 

tapered bearings supported on the shaft. The test bearing 

was at the load cylinder end and the slave bearing at the 

motor end. The test and support bearings are loaded in the 

axial direction with a hydraulic load cylinder. A 200 hp 

electric motor is used to drive the test system to the required 

rpm. There is a direct drive connection from the motor to 

the bearing shaft, with a small coupling shaft connecting the 

two that compensates for minor misalignment. Mobil Jet II 

oil (per MIL-L-23699 specifications) was used as the 

lubricant. Bearing geometry information is listed in Table 1. 

During failure progression tests, each bearing is loaded to 

an axial load of 40,000 lbf. The cylinder is applying the 
NASA/TM—2006-214082 2

Figure 1.—Tapered roller bearing test rig. 

TABLE 1.—BEARING DIMENSIONS 

No. of 

rolling 

elements 

Mean roller 

diameter 

(in.) 

Brg. pitch 

diameter 

(in.) 

Contact 

angle 

(deg) 

29 1.21 14.72 13.07 

40,000 lbf which is seen by each bearing. The axial dynamic 

load thrust rating for each bearing is 28,559 lbf. The shaft 

speed is 3200 rpm. Tests are performed until a significant 

amount of debris is measured by the ODM. The test rig is 

instrumented to measure cup temperatures, input oil 

temperatures, oil flow rates, speed and torque. Vibration 

data was also collected from accelerometers located on the 

test bearing housing in the radial, axial horizontal and 

vertical positions. 

Oil debris data were collected from a 1-1/4 in. ODM 

installed downstream of the bearing housing. A 120 μm 

screen mesh was installed downstream of the ODM. A 

10 μm filter was also installed on the input oil line before 

the oil entered the bearings. The ODM measures the change 

in a magnetic field caused by passage of a metal particle 

where the amplitude of the sensor output signal is 

proportional to the particle mass. The sensor counts the 

number of particles, their approximate size based on user 

defined particle size ranges, and calculates an accumulated 

mass (ref. 11). For these experiments, 16 size ranges, 

referred to as bins, were defined. Based on the bin 

configuration, the average particle size for each bin is used 

to calculate the cumulative mass for the experiment. The 

particle is assumed to be a sphere with a diameter equal to 

the average particle size. Table 2 lists the 16 particle size 

ranges and the average particle size used to calculate 

accumulated mass during bearing tests. Previous research 

verified accumulated mass is a good predictor of pitting 

damage and identified threshold limits that discriminate 

between stages of pitting on spur gears (ref. 6). This method 

will be applied to the data collected during the failure 

progression tests. 

TABLE 2.—FERROUS OIL DEBRIS PARTICLE SIZE RANGES

Bin Bin range, 

μm Average Bin Bin range, μm Average 

1 250–275 263 9 625–675 650 

2 275–325 300 10 675–725 700 

3 325–375 350 11 725–775 750 

4 375–425 400 12 775–825 800 

5 425–475 450 13 825–875 850 

6 475–525 500 14 875–925 900 

7 525–575 550 15 925–975 950 

8 575–625 600 16 975–1016 995 

An alternative technique recommended by the ODM 

manufacturer for setting oil debris mass alarm limits based 

on bearing damage will also be applied to the failure 

progression data (ref. 12). This technique uses the bearing 

geometry to set an alarm threshold based on the total 

accumulated mass of the debris, Malarm, as calculated in 

equation (1) below. This mass alarm limit is based on outer 

race damage, in which the outer race spall angle is large 

enough to allow two balls in the damaged portion at the 

same time. The calculation for this technique is shown 

below: 

360

alarm d d

b

M K PB

N = (1) 

where 

Nb = number of rolling elements = 29 

Pd = bearing pitch diameter (in.) = 14.72 

Bd = rolling element mean diameter (in.) = 1.21 

K = constant for roller bearings (mg/deg in2

) = 9.31 

The K factor was obtained experimentally by the sensor 

manufacturer based on data collected from over 40 bearing 

failures (ref. 12). A K factor of 9.31 for roller bearings was 

used for this application. A mass alarm value of 2058 mg 

was calculated for the roller bearing. The manufacturer also 

provides a method to calculate mass warning, 0.1 times the 

mass alarm, 205.8 mg, to indicate initial spall development. 

Results and Discussion 

The analysis discussed in this section is based on data 

collected during testing of four tapered roller bearings, one 

healthy bearing and three predamaged bearings. The 

principal focus of this research is the detection of damage 

progression on tapered roller bearings. The types of damage 

observed on the bearings included spalling, peeling and 

damage from foreign material. The photos of the amount of 

damage observed during each failure progression test and 

the analysis of the oil debris will be discussed in the 

following sections. 

Bearing Test 1.—After the bearing races were damaged 

by running in a contaminated oil containing steel powder 

particles for 2000 revolutions, they were then ultrasonically 

Support 

Bearings 

Test 

Bearings 

Load 

Cylinder 

To ODM
NASA/TM—2006-214082 3

cleaned and installed in the test rig. The bearings were 

removed for inspection two times during the failure 

progression test. At test completion the bearings had been 

subjected to 81 million cycles. Figure 2 is a plot of the 

accumulated mass measured by the ODM during test 1. The 

boxes labeled 1 and 2 identify when the bearings were 

removed for inspection. Table 3 is a summary of the particle 

counts and accumulated mass measured in each bin prior to 

each inspection interval and at test completion. 

TABLE 3.—TEST 1 PARTICLE DISTRIBUTION 

AND ACCUMULATED MASS 

Bin Bin range, 

μm 

Inspect 

1 

Inspect 

2 

Test 

Complete 

1 250–275 22 62 592 

2 275–325 94 210 2424 

3 325–375 54 143 1858 

4 375–425 57 116 1215 

5 425–475 16 44 741 

6 475–525 17 48 468 

7 525–575 8 24 333 

8 575–625 14 20 256 

9 625–675 4 9 168 

10 675–725 9 12 146 

11 725–775 1 2 75 

12 775–825 0 2 72 

13 825–875 2 3 47 

14 875–925 0 3 47 

15 925–975 0 0 0 

16 975–1016 6 9 221 

Mass (mg) 117.1 245.6 3752.6 

Photos of the damaged components were taken at each 

inspection interval and at test completion. Photos of the 

damage are shown in figures 3 through 6. After the first 

inspection interval, spalling was observed on several of the 

rollers and peeling was observed on the cup and cone. The 

spalls on the rollers increased in size during the second 

inspection, but the peeling on the cup and cone did not 

appear to increase significantly. At test completion, the 

spalls on the rollers increased significantly, the peeling on 

the cup and cone did not change, spalling began to occur on 

the cone and cage damage was also observed. It should be 

noted that the cage material of all bearings tested was silver, 

and nonferrous particles were not analyzed during these 

tests. 

0

1000

2000

3000

4000

2/3/05

12:00 PM

2/8/05

12:00 PM

2/13/05

12:00 PM

2/18/05

12:00 PM

2/23/05

12:00 PM

2/28/05

12:00 PM

3/5/05

12:00 PM

Time

ODM Mass (mg)

1 2

Figure 2.—Mass measured by the ODM during test 1. 

Figure 3.—Test 1 roller damage. 

Figure 4.—Test 1 cup damage. 
NASA/TM—2006-214082 4

Figure 5.—Test 1 cone damage. 

Figure 6.—Test 1 cage damage. 

Bearing Test 2.—After the bearing was predamaged by 

applying hardness dents to the cone, the bearings were 

loaded and installed in the test rig. The bearings were 

removed for inspection four times during the failure 

progression test. At test completion the bearings had been 

subjected to 92 million cycles. Figure 7 is a plot of the 

accumulated mass measured by the ODM during test 2. The 

boxes labeled 1 through 4 identify when the bearings were 

removed for inspection. Table 4 is a summary of the particle 

counts and accumulated mass measured in each bin prior to 

each inspection interval and at test completion. 

Photos of the damaged components were taken at each 

inspection interval and at test completion. Photos of the 

damage are shown in figures 8 to 12. After the first 

inspection interval, wear lines were observed on the rollers, 

and the dents slightly increased in size. During inspection 2, 

small pits were observed on the cup, no significant damage 

was observed on the rollers. At inspection interval 3, a large 

spall was observed on the cone and a few more wear lines 

on the rollers. At inspection interval 4, the cone spall 

increased in size. At test completion the cone spall doubled 

in size, the small pits observed on the cup at inspection 2 

remained the same, and cage damage was observed. 

0

500

1000

1500

2000

2500

3/23/05 

12:00 PM

4/12/05 

12:00 PM

5/2/05 

12:00 PM

5/22/05 

12:00 PM

6/11/05 

12:00 PM

7/1/05 

12:00 PM

Time and Date

ODM Mass (mg)

 1 2

 3

 4

Figure 7.—Mass measured by the ODM during test 2.
NASA/TM—2006-214082 5

TABLE 4.—TEST 2 PARTICLE DISTRIBUTION AND ACCUMULATED MASS

Bin Bin range, 

μm 

Inspect 

1 

Inspect 

2 

Inspect 

3 

Inspect 

4 

Test 

Complete 

1 250–275 0 4 45 116 350 

2 275–325 4 26 235 532 1765 

3 325–375 2 10 156 342 1298 

4 375–425 0 8 115 239 762 

5 425–475 2 8 74 152 456 

6 475–525 1 3 57 116 317 

7 525–575 2 7 36 78 215 

8 575–625 1 1 26 55 147 

9 625–675 1 4 22 39 106 

10 675–725 0 2 12 29 74 

11 725–775 0 3 12 25 54 

12 775–825 0 1 9 17 36 

13 825–875 0 0 5 17 31 

14 875–925 0 1 4 12 33 

15 925–975 0 0 0 0 0 

16 975–1016 0 1 24 52 109 

Mass (mg) 5.4 38.7 394.0 860.4 2265.4 

Figure 8.—Test 2 roller damage. 

Figure 9.—Test 2 cone damage. 
NASA/TM—2006-214082 6

Figure 10.—Test 2 cone damage. 

 

Figure 11.—Cup damage at test completion, observed Figure 12.—Test 2 cage damage at test completion. 

 after inspection 2, but did not progress. 

Bearing Test 3.—After the bearing was predamaged by 

applying hardness dents to the cone, the bearings were 

loaded and installed in the test rig. The bearings were 

removed for inspection three times during the failure 

progression test. At test completion the bearings had been 

subjected to 168 million cycles. Figure 13 is a plot of the 

accumulated mass measured by the ODM during test 3. The 

boxes labeled 1 through 3 identify when the bearings were 

removed for inspection. Table 5 is a summary of the particle 

counts and accumulated mass measured in each bin prior to 

each inspection interval and at test completion. 

Photos of the damaged components were taken at each 

inspection interval. Photos of the damage are shown in 

figure 14. After the first inspection interval, no damage was 

observed on the bearing. In order to expedite failure, the 

raceway was again damaged near the original predamage 

hardness dents. During inspection 2, pitting damage began 

to occur near the location of the predamage dents and 

damage placed on the cone during inspection 1. At 

 inspection interval 3, the damage observed on the cone 

during inspection 2 grew into a large spall. At test 

completion, the initial spall grew slightly and another spall 

occurred 90° from the original spall and wear lines were 

observed on the rollers. 

0

100

200

300

400

500

600

700

8/3/05

8:30 AM

8/18/05

8:30 AM

9/2/05

8:30 AM

9/17/05

8:30 AM

10/2/05

8:30 AM

10/17/05

8:30 AM

11/1/05

8:30 AM

Time and Date

ODM Mass (mg)

 1

 2

 3

Figure 13.—Mass measured by the ODM during test 3. 
NASA/TM—2006-214082 7

TABLE 5.—TEST 3 PARTICLE DISTRIBUTION AND ACCUMULATED MASS 

Bin Bin range, 

μm 

Inspect 

1 

Inspect 

2 

Inspect 

3 

Test 

Complete 

1 250–275 12 21 44 70 

2 275–325 46 85 184 305 

3 325–375 29 62 146 234 

4 375–425 22 39 94 147 

5 425–475 6 13 42 86 

6 475–525 8 14 43 72 

7 525–575 8 16 31 44 

8 575–625 4 9 23 38 

9 625–675 0 4 18 29 

10 675–725 1 6 22 31 

11 725–775 0 2 12 20 

12 775–825 1 2 4 8 

13 825–875 3 4 6 8 

14 875–925 1 2 4 7 

15 925–975 0 0 0 0 

16 975–1016 0 11 23 38 

Mass (mg) 46.0 142.9 353.5 573.5 

Figure 14.—Test 3 cone damage. 

Baseline Test.—A healthy set of bearings was also tested 

to determine baseline wear debris. The healthy set of 

bearings was installed at the completion of test 2. The load 

was gradually increase during testing. Figure 15 is a plot of 

the accumulated mass measured by the ODM during the 

baseline test. Table 6 is a summary of the particle counts 

and accumulated mass measured at test completion. The 

amount of debris detected was significant during this break 

in period. 

0

50

100

150

200

7/20/05 9:00 AM 7/20/05 9:00 PM 7/21/05 9:00 AM 7/21/05 9:00 PM

Time and Date

ODM Mass (mg)

Figure 15.—Mass measured during baseline test. 

TABLE 6.—BASELINE TEST PARTICLE 

 DISTRIBUTION AND MASS 

Bin Bin range, 

μm 

Test 

Complete 

1 250–275 18 

2 275–325 92 

3 325–375 113 

4 375–425 63 

5 425–475 46 

6 475–525 28 

7 525–575 17 

8 575–625 14 

9 625–675 11 

10 675–725 8 

11 725–775 7 

12 775–825 7 

13 825–875 2 

14 875–925 3 

15 925–975 0 

16 975–1016 4 

Mass (mg) 183.5 
NASA/TM—2006-214082 8

Due to the fact that different bearing components 

experienced different levels of damage during each test, 

particle size distributions were plotted to determine if 

particles distributions vary when different components in 

the bearing are damaged. Figures 16 through 19 are 

histograms of the three bearing tests and the baseline tests. 

The histograms show the frequency particles were measured 

within each size distribution. The histograms indicate 

inspection intervals and test completion particle 

distributions. It should be noted that when performing a 

particle distribution analysis on the oil debris data, the bin 

size ranges are critical to the results. A brief review of the 

figures show particle distributions are very similar at test 

completion and during the inspection intervals when 

spalling occurred on the cage and the rollers. During the 

inspection intervals when spalling damage was observed, 

the maximum particles were observed in the 275 to 325 μ

bin range. During the baseline test, when no damage 

occurred, the maximum particles were observed in the 325 

to 375 μ particle size range. 

Size distribution characteristics calculated from the 

histograms attempted to capture the difference between 

distributions in the baseline test and the failure progression 

tests. Relative skewness and kurtosis of the particle 

distributions were calculated to determine if these statistical 

parameters could be used to differentiate between the 

damaged and undamaged bearing. Skewness is a 

0

50

100

150

200

250

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Inspect 1

Inspect 2

0

500

1000

1500

2000

2500

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Test Complete

Figure 16.—Test 1 particle distributions after 

inspections 1 and 2 and at test completion. 

0

100

200

300

400

500

600

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Inspect 1

Inspect 2

Inspect 3

Inspect 4

0

400

800

1200

1600

2000

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Test Complete

Figure 17.—Test 2 particle distributions after 

inspections 1-4 and at test completion. 

0

50

100

150

200

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Inspect 1

Inspect 2

Inspect 3

0

100

200

300

400

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Test Complete

Figure 18.—Test 3 Particle distributions after 

inspections and at test completion. 
NASA/TM—2006-214082 9

0

20

40

60

80

100

120

250275325375425475525575625675725775825875925975

Average Particle Diameter

No. of Particles

Baseline

Figure 19.—Baseline particle 

distributions at test completion. 

measure of the asymmetry of the histogram. All of the 

particle distributions show positive skewness, the tail on the 

right side of the distribution is longer. Kurtosis is a measure 

of the combined weight of the tails related to the rest of the 

distribution. As the tails become heavier, kurtosis increases. 

The following equations were used to calculate relative 

kurtosis and relative skewness from the particle 

distributions shown in figures 16 through 19 (ref. 13), where 

dj is equal to average bin size, j is equal to the number of 

bins and P[dj] is equal to the particles per average bin size 

divided by total number of particles: 

 Mean particle size = ( ) 1

N

j j j

Ed dPd = = ⎡ ⎤ ∑ ⎣ ⎦ (2) 

( ) ( )

2

1

N

j j

j

Variance d E d P d

=

= − ⎡ ⎤ ∑ ⎣ ⎦ (3) 

( ( ))

4

1

N

Kurtosis d E d P d j j j

⎡ ⎤ = − ∑ ⎢ ⎥ ⎣ ⎦ =

 (4) 

( ( ))

3

1

N

Skewness d E d P d j j j

⎡ ⎤ = − ∑ ⎢ ⎥ ⎣ ⎦ =

 (5) 

 Relative Kurtosis = RK =

( )2

Kurtosis

Variance

 (6) 

 Relative Skewness = RS = ( )3 / 2 Variance

Skewness (7) 

Results of the calculated relative kurtosis (RK) and 

relative skewness (RS) are listed in Table 7. Both calculated 

values were lower during test 2 inspection intervals 1 and 2 

than they were for the baseline test. However, minimal wear 

damage was observed on the rollers during these two 

inspection intervals. Relative kurtosis was also lower for 

test 3 inspection interval 3 and test completion. Further 

analysis is required to determine if an increase in relative 

kurtosis of the particle distributions could be used as an oil 

debris feature for damage indication of the bearing. 

In order to develop an oil debris feature that best predicts 

damage levels to the tapered roller bearings, the bearing 

damage levels or state of the bearing must be defined. Three 

primary states of the bearing health will focus on spalling 

damage: O.K. (no damage); Inspect (initial spalling); 

Damage (severe spalling). Next, initial limits for these 

damage levels must be defined. The oil debris mass data 

from the 3 experiments with damage were plotted on figure 

20. The red diamonds identify the inspection intervals 

during tests. Next, the plot is expanded, with the gear state 

transition regions expanded: O.K. (green); Inspect (yellow); 

Damage (red). Since spalling damage was not observed 

during test 2, inspection intervals 1 and 2, this mass 

(38.7 mg) will be used as the lower limit on the inspect 

region. The upper inspect band will be at 183.5 mg, the 

maximum mass measured at the baseline test completion. 

One interesting observation when reviewing figure 20 is that 

the mass alarm value (Malarm) of 2058 mg and the mass 

warning value 205.8 mg, calculated using the manufacturers 

equation (1), both fall within the damage region. 

Fuzzy logic was used to develop a feature that defines 

damage levels based on the accumulated mass measured by 

the ODM. Fuzzy logic applies fuzzy set theory to data, 

where fuzzy set theory is a theory of classes with unsharp 

boundaries and the data belongs in a set based on its degree 

of membership (ref. 14). The degree of membership can be 

any value between 0 and 1. Membership values based on the 

accumulated mass measured by the oil debris sensor will be 

identified. 

Table 7. Relative Skewness and Kurtosis of Particle Distributions 

Baseline Test 1 

Insp. 1 

Test 1 

Insp. 2 

Test 1 

Complete 

Test 2 

Insp. 1 

Test 2 

Insp. 2 

Test 2 

Insp. 3 

Test 2 

Insp. 4 

Test 2 

Compl.

Test 3 

Insp. 1 

Test 3 

Insp. 2 

Test 3 

Insp. 3 

Test 3 

Compl.

RS 1.70 2.06 2.15 2.01 0.30 1.31 1.82 1.84 2.12 2.00 1.87 1.70 1.74

RK 5.95 7.93 8.81 7.22 1.65 4.19 6.29 6.24 7.95 7.38 6.02 5.55 5.71
NASA/TM—2006-214082 10

The data measured from the oil debris sensor during 

experiments with damage and with no damage was used to 

identify membership functions to build a simple fuzzy logic 

model. Defining the fuzzy logic model requires inputs 

(damage detection features), outputs (state of bearings), and 

rules. Inputs are the levels of damage, and outputs are the 

states of the bearings as discussed in the preceding 

paragraph. Membership values were based on the 

accumulated mass and the amount of damage observed 

during inspection. Membership values are defined for the 

three levels of damage: damage low, damage medium, and 

damage high. Using the Mean of the Maximum (MOM) 

fuzzy logic defuzzification method, the oil debris mass 

measured was input into a simple fuzzy logic model created 

using commercially available software (ref. 15). The 

membership function and the output of this model are 

shown on figure 21. Threshold limits for the accumulated 

mass are identified. Results from the four experiments 

indicate accumulated mass is a good predictor of pitting 

damage on tapered roller bearings and fuzzy logic is a good 

technique for setting threshold limits that discriminates 

between states of pitting wear. 

Conclusions 

The purpose of this research was to first verify, when 

using an inductance type, on-line, oil debris sensor, that 

accumulated mass correlates with tapered roller bearing 

fatigue damage. Then, using accumulated mass as the 

damage feature, a method was proposed and used to set 

threshold limits for damaged bearings that discriminates 

between different levels of pitting damage. In this process, 

the membership functions for each feature state were 

defined based on level of damage. From this data, 

accumulated mass measured by an oil debris sensor 

combined with fuzzy logic analysis techniques can be used 

to predict tapered roller health. Applying fuzzy logic 

incorporates decision-making into the diagnostic process 

that improves fault detection and decreases false alarms 

(ref. 7). 

This approach has several benefits over using the 

accumulated mass and an arbitrary threshold limit for 

determining if damage has occurred. One is that it 

eliminates the need for an expert diagnostician to analyze 

and interpret the data, since the output would be one of three 

states, O.K., Inspect, and Shutdown. Since benign debris 

may be introduced into the system, due to periodic 

inspections, setting the lower limit to above this debris level 

will minimize false alarms. In addition to this, a more 

advanced system can be designed with logic built-in to 

minimize these operational effects. Future tests are required 

to collect data from bearings with initial pitting to better 

define the inspect region and the severity of bearing 

damage. Tests of bearings of different sizes are required to 

determine if a relationship can be developed between 

damage levels and bearing dimensions to minimize the need 

for extensive tests to develop the membership functions for 

the threshold levels. 

0

1000

2000

3000

4000

0 4000 8000 12000 16000 20000

Reading

ODM Mass (mg)

0

100

200

300

400

0 4000 8000 12000 16000

Reading

ODM Mass (mg)

Figure 20.—Oil Debris mass during each experiment. 

0

0.25

0.5

0.75

1

1.25

0 100 200 300 400 500

Oil Debris Mass (mg)

Membership

DL DM DH

0

0.25

0.5

0.75

1

0 100 200 300 400 500

Oil Debris Mass (mg)

Damage Level

Inspect

Shutdown

O.K.

Figure 21.—Fuzzy logic membership values for 

oil debris feature and output of model. 
NASA/TM—2006-214082 11

References 

1. Miller, J.L. and Kitaljevich, D.: In-line Oil Debris 

Monitor for Aircraft Engine Condition Assessment. 

IEEE 0–7803 5846–5/00. 

2. Howard, P.L., and Reintjes, J.: A Straw Man for the 

Integration of Vibration and Oil Debris Technologies. 

Presented at the Workshop on Helicopter Health and 

Usage Monitoring Systems, Melbourne, Australia, 

February 1999. Editor: Graham F. Forsyth. DSTO-GD0197 published by DSTO (Defense Science and 

Technology Organization) Australia, February 1999. 

3. Dubowski, D. and Witwer, D.: Application of 

MetalSCAN Oil Debris Monitor to a Fleet of Sea King 

Helicopters. Presented at the American Helicopter 

Society 60th Annual Forum, Baltimore, MD, June 7–

10, 2004. 

4. Hunt, T.M.: Handbook of Wear Debris Analysis and 

Particle Detection in Liquids. Elsevier Applied Science, 

London, 1993. 

5. Dempsey, P.J.: A Comparison of Vibration and Oil 

Debris Gear Damage Detection Methods Applied to 

Pitting Damage. NASA/TM—2000-210371. 

6. Dempsey, P.J.: Gear Damage Detection Using Oil 

Debris Analysis,” NASA/TM—2001-210936, Sept. 

2001. Published in the Journal of Gear Technology, 

January 2003. 

7. Dempsey, P.J.: Integrating Oil Debris and Vibration 

Measurements for Intelligent Machine Health 

Monitoring. PhD Thesis, The University of Toledo. 

Published in the Journal of the American Helicopter 

Society, April 2004, vol. 49, no. 2. 

8. Dempsey, P.J., Lewicki, D.G., and Decker, H.J.: 

Investigation of Gear and Bearing Fatigue Damage 

Using Debris Particle Distributions, NASA/TM—2004-

212883, May 2004. 

9. Kreider, G.E.: Tapered Roller Bearing Health 

Monitoring Test for Pratt & Whitney. Application 

Development Summary Report Project No. 009921, 

January 2000. 

10. Kreider, G.E.: Tapered Roller Bearing Health 

Monitoring Test II for Pratt & Whitney. Application 

Development Summary Report Project No. 009921 

Supplement 1, December 2000. 

11. Howe, B.; and Muir, D.: In-Line Oil Debris Monitor 

(ODM) for Helicopter Gearbox Condition Assessment. 

AD−a347 503, BFGoodrich. 

12. MetalSCANTM User’s Manual. Setting Alarm Limits, 

C000833, Revision 0, GasTOPS, Ontario, Canada. 

13. Roylance, B.J.: Monitoring Gear Wear Using Debris 

Analysis—Prospects for Establishing a Prognostic 

Method. Proceedings of the 5th International Congress 

on Tribology, vol. 4, June 15, 1989. 

14. Zadeh, Lofti, Fuzzy Logic: Advanced Concepts and 

Structures, New Jersey: IEEE, 1992. 

15. “Fuzzy Logic Toolbox for use with MATLAB,” 

January 1998. 

Biographies 

Paula Dempsey is an Aerospace Research Engineer 

employed by the NASA Glenn Research Center, Cleveland, 

Ohio. She joined NASA in 1991, after performing 

experimental and analytical research work in the area of 

cryogenic fluid management systems for a government 

contractor from 1987 to 1991. She continued her cryogenic 

research until 1995, then moved to Aeronautics research, 

serving as lead project engineer for several Aeronautics 

programs. Since 1998, she has been performing research in 

the area of health monitoring and diagnostics for aerospace 

drive systems and propulsion systems and has published 

over 20 papers in this field. She received her BS in 

Mechanical Engineering, an MS degree in Industrial 

Engineering and a Ph.D. in Engineering Science.

Gary Kreider is a Senior Product Development Specialist 

employed by The Timken Company, Canton, Ohio. He 

joined The Timken Company in 1967 as an application 

engineer working on bearing applications for automotive 

and rotary wing aircraft. In 1982 he joined the Timken 

Company’s Physical Laboratory serving as a project leader 

for bearing development in industrial, agriculture and 

aerospace programs. The latter involving hybrid tapered 

roller bearings for the ART program. In addition, he has 

managed programs for bi-directional tapered roller bearings, 

steel and hybrid ball bearings simulating gas turbine engine 

main shaft applications. He received his BS in Mechanical 

Engineering from Pennsylvania State University 

Tom Fichter is a Senior Product Development Technician 

employed by The Timken Company, North Canton, Ohio. 

He joined Timken in 1999 and provides testing support to 

the product and application testing group while pursuing a 

BS in Mechanical Engineering Technology. 
This publication is available from the NASA Center for AeroSpace Information, 301–621–0390.

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NASA TM—2006-214082

E–15419

WBS 561581.02.07.03

17

Investigation of Tapered Roller Bearing Damage Detection Using Oil

Debris Analysis

Paula J. Dempsey, Gary Kreider, and Thomas Fichter

Roller bearings; Damage detection; Wear debris; Health and usage monitoring systems

Unclassified -Unlimited

Subject Category: 07

Prepared for the 2006 Aerospace Conference sponsored by the Institute of Electrical and Electronics Engineers, Big

Sky, Montana, March 4–11, 2006. Paula J. Dempsey, Glenn Research Center; and Gary Kreider and Thomas Fichter,

The Timken Company, 4500 Mount Pleasant Road, N.W., North Canton, Ohio 44720. Responsible person, Paula J.

Dempsey, organization code RXM, 216–433–3398.

A diagnostic tool was developed for detecting fatigue damage to tapered roller bearings. Tapered roller bearings are

used in helicopter transmissions and have potential for use in high bypass advanced gas turbine aircraft engines.

This diagnostic tool was developed and evaluated experimentally by collecting oil debris data from failure progression

tests performed by The Timken Company in their Tapered Roller Bearing Health Monitoring Test Rig. Failure

progression tests were performed under simulated engine load conditions. Tests were performed on one healthy bearing

and three predamaged bearings. During each test, data from an on-line, in-line, inductance type oil debris sensor was

monitored and recorded for the occurrence of debris generated during failure of the bearing. The bearing was removed

periodically for inspection throughout the failure progression tests. Results indicate the accumulated oil debris mass is

a good predictor of damage on tapered roller bearings. The use of a fuzzy logic model to enable an easily interpreted

diagnostic metric was proposed and demonstrated.