NASA/TM--2002-211126

Integrating Oil Debris and Vibration Gear

Damage Detection Technologies Using

Fuzzy Logic

Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Abdollah A. Afjeh

University of Toledo, Toledo, Ohio

July 2002
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NASA/TM--2002-211126

Integrating Oil Debris and Vibration Gear

Damage Detection Technologies Using

Fuzzy Logic

Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Abdollah A. Afjeh

University of Toledo, Toledo, Ohio

Prepared for the

International 58th Annual Forum and Technology Display

sponsored by the American Helicopter Society

Montreal, Quebec, Canada, June 11-13, 2002

National Aeronautics and

Space Administration

Glenn Research Center

July 2002
NASA Center for Aerospace Information

7121 Standard Drive

Hanover, MD 21076

Available from

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22100

Available electronically at http: / / gltrs.grc._aasa.gov/GLTRS
Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic

Paula J. Dempsey

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Abdollah A. Afjeh

The University of Toledo

Toledo, Ohio 43606

Abstract

A diagnostic tool for detecting damage to spur

gears was developed. Two different measurement

technologies, wear debris analysis and vibration,

were integrated into a health monitoring system for

detecting surface fatigue pitting damage on gears.

This integrated system showed improved detection

and decision-making capabilities as compared to

using individual measurement technologies. This

diagnostic tool was developed and evaluated

experimentally by collecting vibration and oil debris

data from fatigue tests performed in the NASA

Glenn Spur Gear Fatigue Test Rig. Experimental

data were collected during experiments performed

in this test rig with and without pitting.

Results show combining the two measurement

technologies improves the detection of pitting

damage on spur gears.

Introduction

One technology area recommended for helicopter

accident reduction is the design of helicopter Health

Usage Monitoring Systems (HUMS) capable of

predicting imminent equipment failure for

on-condition maintenance and a more advanced

system capable of warning pilots of impending

equipment failure. Today's helicopter health

monitoring systems (HUMS) are not at this level.

Data collected by HUMS is often processed after

the flight and plagued with high false alarm rates

and undetected faults. The current fault detection

rate of commercially available HUMS through

vibration analysis is 70 percent [1]. False warning

rates average 1 per hundred flight hours [2]. Often

these systems are complex and require extensive

interpretation by trained diagnosticians [3].

Transmission diagnostics are an important part of

a helicopter HUMS because helicopters depend on

the power train for propulsion, lift, and flight

maneuvering. In order to predict transmission

failures, the diagnostic tools used in the health

monitoring system must provide real-time

performance monitoring of aircraft operating

parameters and must demonstrate a high level of

reliability to minimize false alarms.

Various techniques exist for diagnosing damage in

helicopter transmissions. The method most widely

used involves vibration. Algorithms are developed,

using vibration data collected from gearbox

accelerometers, to detect when gear damage has

occurred. Oil debris is also used to identify

abnormal wear related conditions at an early stage.

Oil debris monitoring for gearboxes consists mainly

of off-line oil analysis, or plug type chip detectors.

Although not commonly used for gear damage

detection, many engines have on-line oil debris

sensors for detecting the failure of rolling element

bearings. These on-line, inductance type sensors

count the number of particles, their approximate

size, then calculate an accumulated mass.

Integrating the sensors into one system can

potentially improve the detection capabilities and

the probability that damage is detected. Recent

investigations have shown the benefits of using an

oil debris monitor with vibration data to improve

current HUMS, but have not fully demonstrated a

system with improved detection and decisionmaking capability when integrating the two

measurement systems [4], [5].

The objective of the work reported herein is to

improve the detection capability of vibration and oil

based damage detection features by applying

fuzzy logic analysis techniques to gear failure data

collected from the NASA Glenn Spur Gear Fatigue

Rig. A simple model was defined by the fuzzy rules

and the membership functions for the experiments

when pitting damage occurred. The ability to define

valid ranges and limits for each membership

function was found to be critical to the success of

the model at predicting damage.

Vibration data were collected from accelerometers

and used in previously validated gear vibration

diagnostic algorithms. Oil debris data were

collected using a commercially available in-line oil

debris sensor. Oil debris and vibration data will be

integrated using fuzzy logic analysis techniques.

The goal of this research is to provide the end user

with a simple tool to determine reliably the health

of this geared system.

NASA/TM--2002-211126 1
Experimental Investigation

Experimental data were recorded from

24 experiments performed in the Spur Gear

Fatigue Test Rig at NASA Glenn Research Center.

A sketch of the test rig is shown in Figure 1. The

facility operates on the torque regenerative

principle. Torque is applied by a hydraulic loading

mechanism that twists one slave gear relative to its

shaft. The power required to drive the system is

only enough to overcome friction losses in the

system [6]. The test gears are standard spur gears

having 28 teeth, 3.50 inch (8.89 cm) pitch

diameter, and 0.25 inch (0.635 cm) face width. The

test gears are run offset to provide a narrow

effective face width to maximize gear contact

stress while maintaining an acceptable bending

stress. Offset testing also allows four tests on one

pair of gears. Two filters are located downstream

of the oil debris monitor to capture the debris after

it is measured by the sensor.

Fatigue tests were run in a manner that allows

damage to be correlated to the oil debris sensor

data. For these tests, run speed was 10,000 RPM

and applied torque was 53 or 71 ft-lbs. (72 or 96

Nom). Prior to collecting test data, the gears were

run for 1 hour at a torque of 10 ft-lbs. (14 Nom).

Test gears were inspected periodically for fatigue

damage throughout the duration of the test. When

damage was found, the damage was documented

and correlated to the test data based on a reading

number. Reading number refers to the once per

minute data collection rate. Reading number is

equivalent to minutes and can also be interpreted

as mesh cycles equal to reading number times 10 4.

In order to document tooth damage, reference

marks are made on the driver and driven gears

during installation to identify tooth 1. The mating

teeth numbers on the driver and driven gears are

then numbered from this reference. Figure 2

identifies the driver and driven gear with the

gearbox cover removed.

The principal focus of this research is the detection

of pitting damage on spur gears. Pitting is a fatigue

failure caused by exceeding the surface fatigue

limit of the gear material. Pitting occurs when small

pieces of material break off from the gear surface,

producing pits on the contacting surfaces [7].

Gears are run until pitting occurs on one or more

several teeth. Two levels of pitting were monitored,

initial (pits less than 0.0397 cm diameter and cover

less than 25 percent of tooth contact area) and

destructive pitting (pits greater than 0.0397cm

diameter and cover greater than 25 percent of

tooth contact area). If not detected in time,

destructive pitting can lead to a catastrophic

transmission failure if the gear teeth crack.

Slave-system oil-inlet .....

Oil-seal gas flow_..

Viewing port _

_- Drive shaft

1/

Shaft oil

seal

Load ',

pressure

""_ I_.oading vane

Slave gear

Test-lubricant

1I

outlet temperature

measurement local:ion _'

Figure 1: Spur Gear Fatigue Rig Gearbox

NASA/TM--2002-211126 2
Figure 2: Spur Gear Fatigue Rig Gearbox With Cover Removed

Data were collected using vibration, oil debris,

speed and pressure sensors installed on the test

rig. Vibration was measured on the gear housing

and at a support bearing location using miniature,

lightweight, piezoelectric accelerometers. Location

of both sensors is shown in Figure 2. These

locations were chosen based on an analysis of

optimum accelerometer locations for this test rig

[8]. Oil debris data were collected using a

commercially available oil debris sensor that

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 measures the

number of particles, their approximate size (125 to

1000 microns) and calculates an accumulated

mass [9]. Shaft speed was measured by an optical

sensor once per revolution of the shaft. Load

pressure was measured using a capacitance

pressure transducer.

Oil debris sensor, speed, pressure, and raw

vibration data were collected and processed in

real-time using the data acquisition program

ALBERT, Ames-Lewis Basic Experimentation in

Real Time, co-developed by NASA Glenn and

Ames. Oil debris and pressure data were recorded

once per minute. Vibration and speed data were

sampled at 200 KHz for one-second duration every

minute. Vibration algorithms FM4 and NA4

Reset were calculated from this data and recorded

every minute. Time-synchronous averaging was

performed from the raw vibration data for

113 revolutions of the test gear. The signal timesynchronous average is obtained by taking the

average of the signal in the time domain with each

record starting at the same point in the cycle as

determined by the once per gear revolution

tachometer signal [10]. The time-synchronous

average data were then used to calculate the two

vibration diagnostic parameters FM4 and NA4

Reset. FM4 and NA4 are dimensionless

parameters with nominal values of approximately

3. When gear damage occurs, the value increases

for both FM4 and NA4 [11].

FM4 was developed to detect changes in the

vibration pattern resulting from fatigue damage on

a limited number of teeth [12]. The theory behind

FM4 is that for a gear in good condition, the

difference signal would be noise with a Gaussian

amplitude distribution. The standard deviation

should be relatively constant, and normalized

kurtosis indicates a value of three. When a tooth

develops a major defect, a peak or series of peaks

appear in the difference signal, causing the

kurtosis value to increase [10]. One problem with

the FM4 parameter is that it decreases in

sensitivity as the number of peaks of similar

magnitude increase beyond two. For this reason,

NA4 was developed for failures that involve more

than two teeth.

NA4 was developed to detect the onset of fatigue

damage and to continue to react to the damage as

it spreads [13]. However, it does not perform well

under fluctuating load conditions. Preliminary tests

found NA4 sensitive to minor changes in load. NA4

Reset was developed from NA4 for applications

with load fluctuations [14].

NASA/TM--2002-211126 3
The oil debris monitor records counts of particles in

bins set at a particle size range. The particle size is

measured in microns. For these experiments,

16 bins were defined. The range of the bin sizes

in microns is shown in Table 1. Based on the

bin configuration, the average particle size for

each bin is used to calculate the cumulative mass

for the experiment. 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 [15].

During each experiment, measurements from two

accelerometers and an oil debris sensor were

monitored and recorded for the occurrence of

pitting damage. The data measured from the

vibration and oil debris sensors during experiments

with and without damage were used to identify

membership functions to build a simple fuzzy logic

model. Using this fuzzy logic model, and the

vibration and oil debris data, threshold limits were

defined that discriminate between different levels

of pitting wear.

Discussion of Results

The analysis discussed in this section is based on

data collected during 24 experiments, 15 of which

had pitting damage occur. Video inspection images

are available for 13 of the experiments with pitting

damage, 2 were performed prior to installation of

the video inspection system.

Table 2 is a summary of the experiments

performed and a description of the damage. The

second column lists the reading number the pitting

was first observed via video or manual inspection.

Video inspection was used during Experiments 1 to

6 and 18 to 24. Manual inspection was used for

experiments 7 to 17. The "oil debris" column is the

amount of debris measured at this reading. The

last reading collected for this experiment is listed in

the fifth column. All gears were visually inspected

at test completion and the damage description and

amount of debris at this time are listed in the last

2 columns. The damage description gives the

damage observed on the driver (Dr) and driven

(Dn) gears. Damage is defined as initial pitting (ip),

and destructive pitting (de) to the total number of

teeth for each gear. For example, Dr: de 3t, ip allt,

is driver gear had destructive pitting on 3 teeth and

initial pitting on all of the teeth. A detailed

description of the damage to each tooth was

correlated with the video images for each

experiment.

A representative sample of a detailed damage

description for each tooth, and the images

obtained from the video inspection system is

shown in Table 3 and Figure 3. The damage

progression video images of tooth 11 on the driver

and driven gear for Experiment 2 are shown in this

figure. The damage is only shown on less than half

of the tooth because the test gears are run offset

to provide a narrow effective face width to

maximize gear contact stress.

Fuzzy logic techniques were applied to the oil

debris and vibration data in order to build a simple

model that discriminates between stages of pitting

wear. 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 [16]. The

degree of membership can be any value between

0 and 1. The advantage of applying fuzzy logic to

condition based maintenance is that it is flexible,

making allowances for unanticipated behavior.

Bin

Table

Bin range, Average

#m

125-175 150

175-225 200

225-275 250

275-325 300

325-375 350

375-425 400

425-475 450

475-525 500

: Oil Debris Particle Size Ranges

Bin Bin range,

9

10

11

12

13

14

15

16

Average

550

575-625 600

625-675 650

675-725 700

725-775 750

775-825 800

825-900 862.5

900-1016 958

NASA/TM--2002-211126 4
Table 2: Summary of Experiments

1 14369 Dr: de It 15.475 15136 Dr: de 3t, ip allt 36.108

Dn: de It Dn: de3t

2 2199 Dr: 8.934 2444 Dr: de 2t, ip allt 26.268

Dn: de It Dn de 3t, ip 3t

3 2669 Dr: de 2t 8.690 3029 Dr: de 3t, ip allt 14.148

Dn: de 2t Dn: de 3t, ip3t

4 2065 Dr: de 3t 12.132 4863 Dr: de 7t, ip allt 26.227

Dn: Dn de 3t, ip allt

5 2566 Dr: ip 2t 7.413 4425 Dr: de 1l t, ip allt 10.811

Dn: Dn de 10t, ip allt

6 12061 Dr: 14.365 12368 Dr: de lt, ip allt 22.851

Dn: de It Dn de 2t, ip allt

7 13716 Dr: ip It 3.381

Dn ip It

8 5181 Dr: ip 2t 6.012 5314 Dr: de 6t, ip8t 19.101

Dn: ip 3t Dn de6t, ip7t

9 29866 No damage 2.359

10 20452 No damage 5.453

11 204 No damage 0.418

12 15654 No damage 2.276

13 25259 No damage 3.159

14 5322 No damage 0

15 21016 No damage 0.125

16 380 No damage 0.099

17 21066 No damage 0.064

18 888 Dr: de 6t, ip allt 22.541

Dn de 4t, ip allt

19 199 Dr: de 3t, ip allt 11.230

Dn de lt, ip allt

20 1593 Dr: de lt, ip allt 5.346

Dn ip allt

21 317 Dr: de It 4.04 514 Dr: de 2t, ip allt 17.912

Dn: de It Dn: de 2t, ip allt

22 838 Dr: ip 5t 7.224

Dn: de 3t, ip allt

23 10688 Dr: de 2t, ip allt 6.399

Dn: de lt, ip allt

24 7170 Dr: de It 6.186 7224 Dr: de lt, ip allt 9.681

Dn: Dn: ip allt

Note: ip=initial pitting; de=destructive pitting; Dr=driver gear; Dn=driven gear; Xt=number of teeth with damage

NASA/TM--2002-211126 5
Table 3: Damage Description for Experiment 2

Read _g D_mage DeS_ p! !eeth Damaged !ee!h Damaged

N _ Dii_ G_a i

 !meimi i'

1573 Run-in Wear All All

Wear All

2199 All

Destructive Pitting 11

Wear All

2296 All

Destructive Pitting 10, 11

Wear All All

2444 Initial Pitting All 10, 11, 14

Destructive Pitting 10, 11 10, 11, 14

Rdg Rdg Rdg Rdg Rdg

0 1573 2199 2296 2444

gearDriver ! :× x_

Figure 3: Damage Progression of Driver/Driven Tooth 11 for Experiment 2

Mamdani's fuzzy inference system is the most

common seen fuzzy methodology and used for this

application [17]. It is based on the paper on fuzzy

algorithms for decision processes [18]. In the

Mamdani type inference systems the output

membership functions are fuzzy sets. The process

is detailed below [19]:

1. fuzzify inputs or fuzzification: converts each

piece of input data to degrees of membership

by a lookup in one of several membership

functions•

2. apply fuzzy operator: AND = minimum; OR =

maximum

3. apply implication methods: apply weight to

rule; output fuzzy set is truncated and scaled•

4. aggregate all outputs: aggregation is the

process by which fuzzy sets represent the

outputs of each rule and are combined into a

single fuzzy set.

5. defuzzify: output is a single number• Middle of

maximum (the average of the maximum value

of the output set)

Defining the fuzzy logic model requires inputs

(damage detection features), outputs (state of

gear), and rules• Commercially available software

was used to build the model because it was a

convenient tool for mapping an input space to an

output space and creating and editing fuzzy

inference systems [20]. Input space for this model

was defined as damage low (DL), damage medium

(DM), and damage high (DH), indicated by the

following features: oil debris mass (DL, DM, DH),

NA4 Reset (DL, DH), and FM4 (DL, DH). The

output space for this model was defined as the

state of the gear. The 3 states of the gear to

predict with this model were identified as: O.K. (no

gear damage); Inspect (initial/destructive pitting);

Shutdown due to damage (severe destructive

pitting)• The Mean of the maximum (MOM) was

chosen as the defuzzification method• MOM was

chosen because it gave the most plausible results

for this application• The MOM method finds the

output with the maximum membership and takes

the x-axis average of all points with this maximum

NASA/TM--2002-211126 6
membership value. If there is more than one point

that has maximum degree of membership, the

mean of the points are taken. The membership

functions were based on the data collected during

experiments 1 to 17.

For the oil debris sensor, membership values were

based on the accumulated mass and the amount

of damage observed the teeth via video or visual

inspection. Membership values are defined for

3 levels of damage: damage low (DL), damage

medium (DM), and damage high (DH) and are

shown in Figure 4. The process used to define

membership functions for the oil debris sensor are

published in an earlier paper and indicate

accumulated mass is a good predictor of pitting

damage on spur gears and fuzzy logic is a good

technique for setting threshold limits that

discriminates between states of pitting wear [15].

FM4 is the vibration feature developed to detect

changes in the vibration pattern due to damage on

a limited number of teeth. When gear damage

occurs, the FM4 value increases, and then

decreases as it progresses to a number of teeth.

FM4 was calculated for the accelerometers located

on the bearing support and the housing. The

maximum value of FM4 measured by the two

accelerometers was used for further analysis. FM4

membership values were defined by looking at the

maximum FM4 value within each inspection

interval. Membership values are defined for

2 levels of damage: damage low (DL) and damage

high (DH). The membership function for FM4 is

shown in Figure 5. Due to FM4's insensitivity to

damage progression, logic is programmed into the

model to freeze the FM4 when it exceeds 7.68.

NA4 Reset, like FM4, is less sensitive to damage

as it progresses to a number of teeth and

increases in severity. Although, the magnitude of

NA4 Reset is significantly larger than FM4 when

pitting damage begins to occur, like FM4, NA4

reset decreases as damage progresses to a

number of teeth. NA4 was calculated for the

accelerometers located on the bearing support and

the housing. The maximum value of NA4

measured by the two accelerometers was used for

further analysis. NA4 membership values were

defined by looking at the maximum NA4 value

within each inspection interval. Membership values

are defined for 2 levels of damage: damage low

(DL) and damage high (DH). The membership

function for NA4 is shown in Figure 6. Due to

NA4's insensitivity to damage progression, logic is

programmed into the model to freeze the NA4

when it exceeds 12.60.

The degree of membership for the output of the

fuzzy model is shown in Figure 7. The output is the

status or state of the gear: O.K. (no gear damage);

Inspect (initial pitting); Damage (destructive

pitting). The rules defined for the model are listed

in Table 4. Using the membership values and rules

for the vibration and oil debris features, and the

Mean of the Maximum (MOM) fuzzy logic

defuzzification method, a simple fuzzy logic model

was developed. The input/output data to the fuzzy

model for each experiment will be discussed in the

following paragraphs.

1.00

0.75

,.O

0.50

0.25

0.00

DL DM DH

t I I {

0 5 10 15 20 25 30 35

Oil debris mass (rag)

Figure 4: Membership Values for Oil Debris Feature

40

NASA/TM--2002-211126 7
1.0

._ 0.8

0.6

0.4

0.2

0.0

DL DH

4 6 8

FM4

Figure 5: Membership Values for FM4 Feature

10

1,0--

•_ 0.8 -

,.l=

.m

0.6- ,.0

E

0.4-

0.2 --

0.0

0

DL DH

I _ [ I I t

5 10 15 20 25 30 35 40 45

NA4 Reset

Figure 6: Membership Values for NA4 Feature

I

50 55

1.0

._ 0.8

,X3 0.6

E

0.4

0.2

0.0

0.0

Inspect

0.2 0.4 0.6

Status of gear

Figure 7: Output of Fuzzy Logic Model

0.8

Shutdown

1.0

NASAFFM--2002-211126 8
Table 4: Rules for Fuzzy Logic Model

1. If

2. If

3. If

4. If

5. If

6. If

7. If

8. If

9. If

10. If

11. If

12. If

FM4 is DL

FM4 is DH

FM4 is DL

FM4 is DL

FM4 is DL

FM4 is DH

FM4 is DH

FM4 is DH

FM4 is DH

FM4 is DH

FM4 is DL

FM4 is DL

and (NA4 is DL) and

and (NA4 is DH) and

and (NA4 is DL) and

and (NA4 is DH) and

and (NA4 is DL) and

and (NA4 is DL) and

and (NA4 is DL) and

and (NA4 is DH) and

and (NA4 is DL) and

and (NA4 is DH) and

and (NA4 is DH) and

and (NA4 is DH) and

debris is DL) then (output is O.K.)

debris is DH) then (output is SHUTDOWN)

debris is DM) then (output is INSPECT)

debris is DL) then (output is O.K.)

debris is DH) then (output is INSPECT)

debris is DL) then (output is O.K.)

debris is DM) then (output is INSPECT)

debris is DL) then (output is INSPECT)

debris is DH) then (output is SHUTDOWN)

debris is DM) then (output is INSPECT)

debris is DH) then (output is SHUTDOWN)

debris is DM) then (output is INSPECT)

Figures 8 through 12 are representative plots for

5 of the 20 experiments. Each figure is comprised

of 2 plots. The plot on the top is a plot of the

3 features measured during each experiment. FM4

and NA4 Reset correspond to the left Y-axis, the

accumulated mass measured by the oil debris

sensor corresponds to the right Y-axis. These

features are input into a simple fuzzy logic model.

The plot on the bottom is the fuzzy logic output.

The triangles on the X-axis correspond to when

video inspection was performed. The background

colors in different shades of gray indicate the O.K.,

inspect, and shutdown due to damage states.

A short description of Figures 8 through 12 will

follow. The results of experiment 2 are plotted on

Figure 8. Destructive pitting was first observed on

one tooth of the driven gear at reading 2199 and

the output plot indicates to inspect the gears. As

the damage increases, the inspect changes to

shutdown for this experiment. Figure 9 presents

the results of experiment 3. Destructive pitting was

first observed on two teeth of both the driven and

driver gear at reading 2669 and the output plot

indicates to inspect the gears. As the damage

increases, the inspect changes to shutdown for this

experiment. Experiment 12 is plotted on Figure 10.

No damage occurred during this experiment, and

the output plot remains in the green region.

Experiment 8 is plotted on Figure 11. Initial pitting

was first observed on 2 driver teeth and 3 driven

teeth at reading 5181 and the output plot indicates

to inspect the gears. As the damage increases, the

inspect changes to shutdown for this experiment.

Experiment 18, not used to define the membership

functions, is plotted on Figure 12. At test

completion, destructive pits were observed on 6 of

the driver teeth and 4 of the driven teeth.

After review of the data from these experiments,

the advantage of integrating the features of

different measurement technologies into a simple

fuzzy logic model is evident. The output gives clear

information to the end user when making a

decision based on the data. The model developed

incorporates the expert knowledge of the

diagnostician into a system that can be used to

make clear decisions on the status of the geared

system.

Several observations are worth noting after careful

analysis of the data. The first is that the oil debris

feature was more reliable than the vibration

features for detecting pitting fatigue failure of spur

gears. The vibration features were more sensitive

to the environment (operational effects, location,

sampling rates, etc.) and these sensitivities were

more difficult to quantify or correct for in the field.

Another observation is that a technique for setting

accurate threshold limits for vibration algorithms

was not clearly defined in the literature [10], [11],

[13], [21], [22], [23]. It appears to be a trial and

error method that changes for each experiment

and each test rig. This makes it very difficult to

quantify the false alarms and missed hits using the

individual algorithms. If the threshold limits for the

vibration algorithms are set at any number above

the nominal value of 3.0, the false alarms would

dominate [10], [12], [13], [21].

In comparison, the thresholds for this analysis

were determined based on membership functions

defined for 17 experiments with varied operational

conditions. The process used to define

membership functions for the vibration algorithms

was an attempt to intelligently define threshold

limits. Setting thresholds using membership

functions gives the end user more flexibility in

defining threshold limits based on levels of

damage. However, this method also has its

limitations in that it requires several sets of

damage data to refine the limits.

NASA/TM--2002-211126 9
20

16

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8

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1.0

0.8

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0

0.0

0

................................... -- FM4' .................................... '....................................................................... _..................................... 30

-- NA4 Reset

Oil debris 24

18

..

_,,_ _ _.,_,,,,,,_:_,__, _, ::1i i_IEii_'j{ 'i" 12_

............................................................................................................................. 0

500 1000 1500 2000 2500

Reading number

500 1000 1500 2000 2500

Reading number

Figure 8: Experiment 2 Features and Model Output

NASA/TM--2002-211126 10
(D

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Z

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20 :: : ....... 20

15!-

10

FM4

NA4 Reset

Oil debris

15

10

em

E

_d

r_

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1.0

0.8

0.6

0.4

0.2

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o i_7_ ............... o

0 500 1000 1500 2000 2500 3000 3500

Reading number

212121212121212121212121212121212121212111

Iiiiiiiiiiiiiiiiiiiiiiii0iKiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii

0 500 1000 1500 2000 2500 3000 3500

Reading number

Figure 9: Experiment 3 Features and Model Output

NASA/TM--2002-211126 11
r_

<

Z

i0

4

2

FM4

NA4 Reset

Oil debris

1.0

0.8

0.6

©

0.4

0.2

0.0

0

0 4000 8000 12 000

Reading number

0.0

16 000

iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii @iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

4000 8000 12 000 16 000

Reading number

Figure 10: Experiment 12 Features and Model Output

NASA/TM--2002-211126 12
©

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20

15

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<

ZI0

5

0

0

1.0

0,8

0.6

0.4

0.2

0.0

0

-- FM4

-- NA4 Reset

Oil debris

I _ i 1 i

1000 2000 3000 4000 5000

Reading number

1000 2000 3000 4000 5000

Reading number

Figure 11 Experiment 8 Features and Model Output

2O

415

"410

0

6000

6000

C:

©

NASA/TM--2002-211126 13
20 .................... ,".................. _............... ,................... _.................... _................... ,................... ,................... _.................... 25

-- FM4

........NA4 Reset

16 -- Oil debris 20

12

"'O

k_

0

0 100 200 300 400 500 600 700 800 900

Reading nLnnber

1.0

0.8

= 0.6

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0.4

0.2 oK

0.0

0 100 200 300 400 500 600 700 800 900

Reading number

Figure 12: Experiment 18 Features and Model Output

0

NASA/TM--2002-211126 14
Conclusions

The integration of two measurement technologies,

oil debris analysis and vibration, results in a

system with improved damage detection and

decision-making capabilities. Vibration and oil

debris data were collected from experiments in the

NASA Glenn Spur Gear Fatigue Rig. Using fuzzy

logic techniques applied to the oil debris and

vibration data, a simple system model was

developed that discriminates between stages of

pitting wear. Results indicate combining the

vibration and oil debris measurement technologies

improves the detection of pitting damage on spur

gears. As a result of this research, the diagnostic

tools used for damage detection in the NASA

Glenn Spur Gear Fatigue Rigs have been

significantly improved.

Several other findings were made that will impact

the development of health monitoring tools for

geared systems. The first being, oil debris analysis

is more reliable than vibration analysis for

detecting pitting fatigue failure of spur gears. The

second finding is that some vibration algorithms

are as sensitive to operational effects as they are

to damage. The third finding is that vibration

algorithms FM4 and NA4 Reset do not indicate

damage progression, but the increase in oil debris

mass is related to damage progression. The fourth

finding is that clear threshold limits must be

established by the developer of the diagnostic

tool if it is to be applied to other systems. The

development of membership functions for each

parameter will improve this process. It also enables

the end user to replace these parameters with their

own by adjusting the membership functions. The

fifth finding relates to the human factors aspect of

diagnostic tool development. As a diagnostician, it

is important to identify the end user of the

diagnostic tool early on in the process so that he or

she can use the diagnostic tool to make clear

decisions on the health of the geared system.

References

[1] Larder, Brian D.: Helicopter HUM/FDR:

Benefits and Developments. Proceedings of

the American Helicopter Society 55th Annual

Forum, Montreal, Canada, May 1999.

[2] Stewart, Ronald M. and Ephraim, P.:

Advanced HUMS and Vehicle Management

Systems Implemented Through an IMA

Architecture. Proceedings of the American

Helicopter Society 53rd Annual Forum,

vol. 53N2, pp. 1257-1266, 1997.

[3] Pouradier, Jean-Marc, and Trouv6, M: An

assessment of Eurocopter experience in

HUMS development and support.

Proceedings of the American Helicopter

Society 57th Annual Forum, Washington, DC,

May 2001.

[4] Howard, Paul L. and Reintjes, J.F.: A Straw

Man for the Integration of Vibration and Oil

Debris Technologies. Helicopter Health and

Usage Monitoring Systems Workshop,

G.G. Forsyth, ed., Defense Science and

Technology Organization General Document

197, no. 1, pp. 131-136, February 1999.

[5] Byington, C.S.; Merdes, T.A.; and Kozlowski,

J.D. (1999): Fusion Techniques for Vibration

and Oil Debris/Quality in Gearbox Failure

Testing. Proceedings of the Condition

Monitoring International Conference,

M.H. Jones and D.G. Sleeman, eds.,

Coxmoor Publishing, Oxford, England,

pp. 113-128, April 1999.

[6] Lynwander, Peter: Gear Drive Systems:

Design and Application. Marcel Dekker, New

York, NY, 1983.

[7] Townsend, Dennis P.: Dudley's Gear

Handbook. McGraw-Hill, New York, NY,

1991.

[8] Zakrajsek, James J.; Townsend, D.P.;

Oswald, F.B.; and Decker, H.J.: Analysis and

Modification of a Single-Mesh Gear Fatigue

Rig for Use in Diagnostic Studies. NASA TM105416, May 1992.

[9] Howe, B.; and Muir, D.: In-Line Oil Debris

Monitor (ODM) for Helicopter Gearbox

Condition Assessment. AD-a347 503,

Defense Technical Information Center,

Ft. Belvoir, VA, 1998.

[10] Zakrajsek, James J.: An Investigation of Gear

Mesh Failure Prediction Techniques. NASA

TM-102340, November 1989.

[11] Zakrajsek, James J.; Handschuh, R.F.; and

Decker, H.J.: Application of Fault Detection

Techniques to Spiral Bevel Gear Fatigue

Data. NASA TM-106467, January 1994.

[12] Stewart, R.M.: Some Useful Data Analysis

Techniques for Gearbox Diagnostics. Report

MHM/R/10/77, Machine Health Monitoring

Group, Institute of Sound and Vibration

Research, University of Southhampton,

July 1977.

[13] Zakrajsek, J.J.; Townsend, D.P.; and Decker,

H.J.: An Analysis of Gear Fault Detection

Methods as Applied to Pitting Fatigue Failure

Data. NASA TM-105950, January 1993.

NASA/TM--2002-211126 15
[14] Dempsey, P.J. and Zakrajsek, J.J.:

Minimizing Load Effects on NA4 Gear

Vibration Diagnostic Parameter. NASA TM210671, February 2001.

[15] Dempsey, P.J.: Gear Damage Detection

Using Oil Debris Analysis. NASA TM-210936,

September 2001.

[16] Zadeh, Lotfi Asker: Fuzzy Logic Advanced

Concepts and Structures. IEEE Educational

Activities Board. IEEE, Piscataway, N J, 1992.

[17] Mamdani, E.H.; and Assilan, S.: An

Experiment in Linguistic Synthesis With a

Fuzzy Logic Controller. International Journal

of Man-Machine Studies, Vol. 7, No. 1, 1975,

pp. 1-13.

[18] Zadeh, L.A.: Outline of a new approach to the

analysis of complex systems and decision

processes. IEEE Transactions on Systems,

Man, and Cybernetics, Vol. 3, No. 1,

pp. 28-44, Jan. 1973.

[19] Jantzen, Jan: Tutorial on Fuzzy Logic.

Technical University of Denmark, Department

of Automation, Tech. Report No. 98-E 868

(logic), April 16, 1999.

[20] Fuzzy Logic Toolbox for Use With MATLAB®:

MathWorks, Inc., Natick, MA, January 1998.

[21] Zakrajsek, James J.: A Review of

Transmission Diagnostics Research at NASA

Lewis Research Center, NASA TM-106746,

1994.

[22] Zakrajsek, James J.; Decker, H.J.;

Handschuh, R.F.; and Lewicki, D.G.:

Detecting Gear Tooth Fracture in a High

Contact Ratio Face Gear Mesh. NASA TM106822, January 1995.

[23] Zakrajsek, J.J.; Townsend, D.P.; Lewicki,

D.G.; Decker, H.J., and Handschuh, R.F.:

Transmission Diagnostic Research at NASA

Lewis Research Center, NASA TM-106901,

May 1995.

NASA/TM--2002-211126 16
Form Approved REPORT DOCUMENTATION PAGE

OMB No. 0704-0188

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources,

gathering and maintainingthe data needed, and completing and reviewingthe collection of information. Send comments regarding this burden estimate or any other aspect of this

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1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

July 2002 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Integrating Oil Debris and Vibration Gear Damage Detection Technologies

Using Fuzzy Logic

6. AUTHOR(S)

Paula J. Dempsey and Abdollah A. Afjeh

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

John H. Glenn Research Center at Lewis Field

Cleveland, Ohio 44135-3191

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

WU-728-30-10-00

8. PERFORMING ORGANIZATION

REPORT NUMBER

E-12976

10. SPONSORING/MONITORING

AGENCY REPORT NUMBER

NASA TM--2002-211126

11. SUPPLEMENTARY NOTES

Prepared for the International 58th Annual Forum and Technology Display sponsored by the American Helicopter

Society, Montreal, Quebec, Canada, June 11-13, 2002. Paula J. Dempsey, NASA Glenn Research Center; Abdollah A.

Afjeh, University of Toledo, Toledo, Ohio 43606. Responsible person, Paula J. Dempsey, organization code 5950,

216-433-3398.

12a. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category: 01 Distribution: Nonstandard

Available electronically at http:/Igltrs.grc.na_sa.gov/G[2I'RS

This publication is available from the NASA Center for AeroSpace Information, 301-621-0390.

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

A diagnostic tool for detecting damage to spur gears was developed. Two different measurement technologies, wear

debris analysis and vibration, were integrated into a health monitoring system for detecting surface fatigue pitting

damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to

using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by

collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Test Rig.

Experimental data were collected during experiments performed in this test rig with and without pitting. Results show

combining the two measurement technologies improves the detection of pitting damage on spur gears.

14. SUBJECT TERMS

Gears; Transmissions; Oil debris sensor; Damage assessment; Damage detection;

Health monitoring; Pitting fatigue

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION

OF REPORT OF THIS PAGE OF ABSTRACT

Unclassified Unclassified Unclassified

NSN 7540-01-280-5500

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