Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Abdollah A. Afjeh

University of Toledo, Toledo, Ohio

Integrating Oil Debris and Vibration Gear

Damage Detection Technologies Using

Fuzzy Logic

NASA/TM—2002-211126

July 2002
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Paula J. Dempsey

Glenn Research Center, Cleveland, Ohio

Abdollah A. Afjeh

University of Toledo, Toledo, Ohio

Integrating Oil Debris and Vibration Gear

Damage Detection Technologies Using

Fuzzy Logic

NASA/TM—2002-211126

July 2002

National Aeronautics and

Space Administration

Glenn Research Center

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NASA/TM—2002-211126 1 

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 2 

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 

N&m). Prior to collecting test data, the gears were 

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

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 104

. 

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 0.0397 cm 

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. 

Figure 1: Spur Gear Fatigue Rig Gearbox 
NASA/TM—2002-211126 3 

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 4 

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. 

Table 1: Oil Debris Particle Size Ranges 

Bin Bin range, 

μm 

Average Bin Bin range, 

μm 

Average 

1 125–175 150 9 525–575 550 

2 175–225 200 10 575–625 600 

3 225–275 250 11 625–675 650 

4 275–325 300 12 675–725 700 

5 325–375 350 13 725–775 750 

6 375–425 400 14 775–825 800 

7 425–475 450 15 825–900 862.5 

8 475–525 500 16 900–1016 958 
NASA/TM—2002-211126 5 

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

Table 2: Summary of Experiments 

Experiment 

Number 

Rdg Pitting 

First Observed 

Damage 

Description 

Oil Debris 

(mg) 

Rdg at Test 

Completion 

Damage 

Description 

Oil Debris 

(mg) 

1 14369 Dr: de 1t 

Dn: de 1t 

15.475 15136 Dr: de 3t, ip allt 

Dn: de3t 

36.108 

2 2199 Dr: 

Dn: de 1t 

8.934 2444 Dr: de 2t, ip allt 

Dn de 3t, ip 3t 

26.268 

3 2669 Dr: de 2t 

Dn: de 2t 

8.690 3029 Dr: de 3t, ip allt 

Dn: de 3t, ip3t 

14.148 

4 2065 Dr: de 3t 

Dn: 

12.132 4863 Dr: de 7t, ip allt 

Dn de 3t, ip allt 

26.227 

5 2566 Dr: ip 2t 

Dn: 

7.413 4425 Dr: de 11t, ip allt 

Dn de 10t, ip allt 

10.811 

6 12061 Dr: 

Dn: de 1t 

14.365 12368 Dr: de 1t, ip allt 

Dn de 2t, ip allt 

22.851 

7 13716 Dr: ip 1t 

Dn ip 1t 

3.381 

8 5181 Dr: ip 2t 

Dn: ip 3t 

6.012 5314 Dr: de 6t, ip8t 

Dn de6t, ip7t 

19.101 

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 

Dn de 4t, ip allt 

22.541 

19 199 Dr: de 3t, ip allt 

Dn de 1t, ip allt 

11.230 

20 1593 Dr: de 1t, ip allt 

Dn ip allt 

5.346 

21 317 Dr: de 1t 

Dn: de 1t 

4.04 514 Dr: de 2t, ip allt 

Dn: de 2t, ip allt 

17.912 

22 838 Dr: ip 5t 

Dn: de 3t, ip allt 

7.224 

23 10688 Dr: de 2t, ip allt 

Dn: de 1t, ip allt 

6.399 

24 7170 Dr: de 1t 

Dn: 

6.186 7224 Dr: de 1t, ip allt 

Dn: ip allt 

9.681 
NASA/TM—2002-211126 6 

Table 3: Damage Description for Experiment 2 

Reading 

Number 

Run Time (min) 

Damage Description Teeth Damaged 

on Driver Gear 

Teeth Damaged 

on Driven Gear 

1573 Run-in Wear All All 

2199 Wear 

Destructive Pitting All All  11 

2296 Wear 

Destructive Pitting All All  10, 11 

2444 

Wear 

Initial Pitting 

Destructive Pitting 

All 

All 

10, 11 

All 

10, 11, 14 

10, 11, 14 

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 7 

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. 

Figure 4: Membership Values for Oil Debris Feature 
NASA/TM—2002-211126 8 

Figure 5: Membership Values for FM4 Feature 

Figure 6: Membership Values for NA4 Feature

 Figure 7: Output of Fuzzy Logic Model
NASA/TM—2002-211126 9 

Table 4: Rules for Fuzzy Logic Model 

1. If (FM4 is DL) and (NA4 is DL) and (debris is DL) then (output is O.K.) 

2. If (FM4 is DH) and (NA4 is DH) and (debris is DH) then (output is SHUTDOWN) 

3. If (FM4 is DL) and (NA4 is DL) and (debris is DM) then (output is INSPECT) 

4. If (FM4 is DL) and (NA4 is DH) and (debris is DL) then (output is O.K.) 

5. If (FM4 is DL) and (NA4 is DL) and (debris is DH) then (output is INSPECT) 

6. If (FM4 is DH) and (NA4 is DL) and (debris is DL) then (output is O.K.) 

7. If (FM4 is DH) and (NA4 is DL) and (debris is DM) then (output is INSPECT) 

8. If (FM4 is DH) and (NA4 is DH) and (debris is DL) then (output is INSPECT) 

9. If (FM4 is DH) and (NA4 is DL) and (debris is DH) then (output is SHUTDOWN) 

10. If (FM4 is DH) and (NA4 is DH) and (debris is DM) then (output is INSPECT) 

11. If (FM4 is DL) and (NA4 is DH) and (debris is DH) then (output is SHUTDOWN) 

12. If (FM4 is DL) and (NA4 is DH) and (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 10 

Figure 8: Experiment 2 Features and Model Output
NASA/TM—2002-211126 11 

Figure 9: Experiment 3 Features and Model Output 
NASA/TM—2002-211126 12 

Figure 10: Experiment 12 Features and Model Output
NASA/TM—2002-211126 13 

Figure 11: Experiment 8 Features and Model Output
NASA/TM—2002-211126 14 

Figure 12: Experiment 18 Features and Model Output
NASA/TM—2002-211126 15 

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. 

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Architecture. Proceedings of the American 

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vol. 53/V2, pp. 1257–1266, 1997. 

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

assessment of Eurocopter experience in 

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

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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 

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[6] Lynwander, Peter: Gear Drive Systems: 

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

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

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105416, May 1992. 

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Condition Assessment. AD–a347 503, 

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Ft. Belvoir, VA, 1998. 

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TM–102340, November 1989. 

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

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22

Integrating Oil Debris and Vibration Gear Damage Detection Technologies

Using Fuzzy Logic

Paula J. Dempsey and Abdollah A. Afjeh

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

Health monitoring; Pitting fatigue

Unclassified -Unlimited

Subject Category: 01 Distribution: Nonstandard

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.

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.