Health Assessment and Fault Classification of Roller 

Element Bearings

by Andrew J. Bayba, David N. Siegel, Kwok Tom, and Derwin Washington

ARL-TR-6080 July 2012

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Army Research Laboratory

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ARL-TR-6080 July 2012

Health Assessment and Fault Classification of Roller 

Element Bearings

Andrew J. Bayba, David N. Siegel, Kwok Tom, and Derwin Washington

Sensors and Electron Devices Directorate, ARL

Approved for public release; distribution unlimited. 
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Health Assessment and Fault Classification of Roller Element Bearings

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Andrew J. Bayba, David N. Siegel, Kwok Tom, and Derwin Washington

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14. ABSTRACT

Feature extraction, health assessment, and fault classification algorithms were evaluated for ball bearings with three different 

fault types and multiple levels of damage. Data was analyzed for five healthy bearings, and seeded fault bearings with five 

levels of damage for each fault type (ball fault, inner race fault, and outer race fault). A variety of fault analysis techniques 

were used to calculate properties (features) of the data sets, which were then fused together to form the best feature sets for 

fault evaluation. Self-organizing maps were used for health assessment, and a Naïve Bayes classifier was used to determine 

fault type. The results indicate a very good distinction between healthy and faulted bearings, and a good classification of fault 

types. For health assessment, there were good general trends with increasing damage. There was, however, a significant 

amount of scatter, thereby making it difficult to ascertain the precise health of an individual bearing. Although our feature set 

is substantial, it is by no means exhaustive, and one consideration is to seek additional features that may produce a higher 

level of confidence in individual bearing health.

15. SUBJECT TERMS

Bearing health assessment, bearing fault classification, roller element bearings, seeded fault testing

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iii

Contents

List of Figures iv

List of Tables iv

1. Introduction 1

2. Bearings Under Test 1

3. Test Equipment and Instrumentation 3

4. Data 5

5. Analysis 6

5.1 Feature Extraction ...........................................................................................................6

5.2 Feature Selection .............................................................................................................8

5.2.1 For Health Assessment........................................................................................8

5.2.2 For Fault Classification .......................................................................................9

5.3 Health Assessment ........................................................................................................10

5.4 Fault Classification........................................................................................................10

6. Results 11

7. Discussion and Conclusions 14

8. References 16

List of Symbols, Abbreviations, and Acronyms 17

Distribution List 18
iv

List of Figures

Figure 1. Cross section of a ball bearing with nominal seeded fault locations...............................2

Figure 2. Test rig used in performing bearing experiments............................................................4

Figure 3. Mounting locations and orientation of the accelerometers..............................................5

Figure 4. Health assessment and fault classification process. ........................................................6

Figure 5. Bearing health value as a function of the outer race damage level. ..............................12

Figure 6. Bearing health value as a function of the inner race damage level. ..............................13

Figure 7. Bearing health value as a function of the ball damage level. ........................................14

List of Tables

Table 1. Defect frequencies for the bearings. .................................................................................3

Table 2. Data sets............................................................................................................................5

Table 3. Extracted features and methodology.................................................................................8

Table 4. Top features for health assessment. ..................................................................................9

Table 5. Top features for fault classification. ...............................................................................10

Table 6. Classification of fault types (confusion matrix). ............................................................14
1

1. Introduction

The United States Army has a substantial interest in the application of prognostics and 

diagnostics (P&D) for components and systems. The interest is two-fold; the commander in the 

field has an intense interest in operational readiness of assets, while logistical personnel are 

interested in cost efficiency. These can both be optimized by providing an accurate real-time 

health assessment and remaining useful life (RUL) prediction. As a result of these needs, the

U.S. Army Research Laboratory (ARL) has been actively pursuing advancements in P&D. 

Bearings are of particular interest because of their existence in virtually every Army system, and 

the consequent requirement for a large amount of effort dedicated to monitoring them with the 

hope that they are being maintained or replaced at the best time. 

Health assessment algorithms have been under development at ARL using a variety of 

parameters or “features” extracted from several signal processing techniques commonly accepted 

in the assessment of bearing health. This report focuses on the application of these algorithms to 

the results of seeded fault bearing tests, both for evaluation and improvement purposes. We use 

vibration signals from accelerometers mounted in the vicinity of the bearing as the monitoring 

source for our analysis/feature extraction. The uniqueness of this work is in the fusion of a 

variety of features/signal processing techniques with the belief that this wide collection of 

features will provide a better likelihood of capturing any degradation in a bearing and, thus,

produce a more accurate health assessment. In all, there are 288 features used in the signal 

processing—72 individual features for each of four accelerometer channels. The features range 

from simple statistics, such as root mean square (RMS), to rather complex items, such as 

Wavelet Band Energy. The testing procedure and data collected are described in reference 1.

2. Bearings Under Test

All the bearings used in this study were Rexnord ER16K ball bearings. All of the bearings were 

new and most had defects intentionally made in them (seeded faults). There were five good 

bearings and 15 bearings with seeded faults. The faulted bearings consisted of five bearings with 

ball faults, five bearings with inner race faults, and five bearings with outer race faults. The 

bearings of each fault type had five levels of damage of the specific defect. Figure 1 is a crosssection of a bearing showing nominal locations of the seeded faults along with their associated 

components. The bearings were provided as a custom order from SpectraQuest, Inc., who was 

also responsible for applying the faults to the bearings. The bearing parameters are presented

here along with the bearings characteristic frequencies (commonly referred to as fault 

frequencies or defect frequencies).
2

 

Figure 1. Cross section of a ball bearing with nominal seeded fault locations.

For these experiments, the bearing parameters are:

Number of balls, n = 9

Ball Diameter, BD = 0.3125 in

Pitch Diameter, PD = 1.5157 in,

Contact angle, θ = 0

Operating frequency, f = 35 Hz

The formulae for the defect frequencies are:

 

 

 

 

 

 

 (1)

 

 

 

 

 

 

 (2)

 

 

 

 

 

 

 (3)

 

 

 

 

 

 (4)

 

 

 

 

 

 (5)

where

Ball pass frequency inner (BPFI) = the frequency corresponding to a defect on the inner race.

BPFO = the ball pass frequency of the outer race and corresponds to a defect on the outer race.

FTF = the fundamental train frequency, i.e., the frequency of the cage.

BSF = the ball spin frequency, i.e., the rotational frequency of each ball.
3

Rolle = the ball defect frequency; (Rolle = 2 x BSF; a ball defect will strike one of the races at 

this frequency).

The calculated values of the defect frequencies are listed in table 1.

Table 1. Defect frequencies for the bearings.

BPFI BPFO FTF BSF Rolle

190 Hz 125 Hz 13.9 Hz 81.3 Hz 162.5 Hz

3. Test Equipment and Instrumentation

The bearings were placed in service on a test rig manufactured by SpectraQuest Inc. (figure 2). 

The rig is specifically designed for studying defects in machine components and is outfitted with 

mounting holes for accelerometers in positions of interest. As can be seen in figure 1, the rig is a 

complete drivetrain consisting of an electric motor; shaft with weights, pulleys, and belts; a 

gearbox; and a magnetic load. The shaft was supported by two ball bearings near the ends of the 

shaft. The bearing closest to the motor was the bearing under test, while the bearing further from 

the motor was always a known good bearing. The shaft was loaded with two 11-lb cylindrical 

weights that rotate with the shaft; one on either side of the bearing under test. For this study, the 

belts, gearbox, and magnetic load were removed from the drivetrain to reduce the noise from 

vibrations not related to the bearings.
4

Figure 2. Test rig used in performing bearing experiments. 

The test rig was instrumented with a tachometer (supplied with the test rig) and two triaxial 

accelerometers, manufactured by Vibra-Metrics, Inc. Both accelerometers were attached to the 

mounting block of the bearing under test: one directly above the bearing and one to the side of it, 

at 90° to the first accelerometer. The accelerometer placements and axis orientations are shown 

in figure 3. The x-axes of both accelerometers point in the axial direction of the system—i.e., 

parallel to the shaft axis—while the z- and y-axes point in radial directions—i.e., perpendicular 

to the axis of the shaft. All accelerometer channels were active and connected to the data 

acquisition system; however, due to the high sampling rate (100 KSamples/s/channel), only four 

channels could be collected. All channels of the first accelerometer were collected, and the x 

direction channel of the second accelerometer was selected for collection because it had a higher 

amplitude return than the y and z directions.
5

Figure 3. Mounting locations and orientation of the accelerometers.

4. Data

The data was collected from the tachometer and accelerometers for 10 runs of each of the 20 

bearings investigated. Data from the tachometer was not used in this study and will not be 

discussed further here; however, it was retained since it may prove useful in future studies. The 

accelerometer data was collected in the units of g’s of acceleration. The data, as collected, was in 

the binary format of the DAQ hardware and was slightly over 10 s for each run. Exactly 10 s of 

the accelerometer data was extracted from each of these files and was written to ASCII text files 

as four columns of tab delimited data, one column for each accelerometer channel. The data is 

summarized in table 2.

Table 2. Data sets.

Data Set # Files 

Healthy Bearings 50 (10 for each bearing)

Inner Race Defect 50 (10 at each level) 

Outer Race Defect 50 (10 at each level) 

Ball Defect 50 (10 at each level) 
6

5. Analysis

The analysis involved the application of several ARL-developed algorithms to extract features 

from the data, select the most useful features, assess the health of the bearings, and identify the 

fault type of a particular bearing. MATLAB was used for algorithm development and data 

processing. The ARL algorithms include the use of MATLAB built in functions, as well as 

algorithms developed by the Center for Intelligent Maintenance Systems, a consortium of the 

University of Cincinnati, the University of Michigan, and Missouri University of Science and 

Technology. The algorithms developed at ARL will be described in general here. The flow of the 

analysis is depicted in figure 4, and is followed by a description of the steps. 

Figure 4. Health assessment and fault classification process.

5.1 Feature Extraction

Characteristics of the signals, commonly referred to as features, were calculated from the data 

files. A significant amount of research into bearing fault feature generation has been conducted 

over the last few decades. Features deemed useful for this study were determined based on a 

review of the literature. A large selection of features was included, providing a greater likelihood 

that a defect would be identified and also that this work may reveal which features are of greater 

value for these defect types. In all there were 288 features calculated per run (72 per channel for 

each run). The features are described further, along with rationale for using them in table 3. 
7

a. Time Domain Statistics. Statistics of the signals in the time domain were extracted. They 

are easy to calculate, making them amenable to application in the field, and they are known 

to vary with fault level, although not always with high correlation. An additional set of 

statistical features was created by first lowpass filtering the data prior to the calculations,

with the intent of noise reduction. The filter was a fourth-order lowpass Butterworth filter 

with a cut-off frequency of 1500 Hz.

b. FFT Band Energy. It is typical that there is a shift in spectral energy with bearing 

degradation. To capture this effect as features, the frequency spectrum was divided into 

four KHz Bands and the energy in each of these bands was calculated for each signal.

c. FFT Bearing Fault Frequency Energy. As defects appear and develop, the bearing’s defect

frequencies often grow in amplitude. The actual defect frequencies will vary slightly from 

those presented in table 1 due to slippage. Therefore, small bands (±2 Hz) around the

calculated frequencies were used. The largest peak within each of the bands produced the 

features. 

d. Bearing Envelope Analysis. The bearing defect frequencies are often masked by noise and 

other spectral content at their true, relatively low, calculated frequencies. To aid in isolating 

them, high frequency envelope analysis is commonly employed (2). The technique 

involves determining a suitable frequency band, where the signal is amplified. We 

examined several frequency bands and found that the band where the spectral kurtosis was 

highest worked well. The technique then employs demodulating the band pass filtered 

signal and applying the methodology of item c. to produce the features.

e. Spectral Kurtosis. Kurtosis as a function of frequency highlights which frequency band 

shows the most variation in the signal, being related to the impact of the bearing against the 

defect, and provides a good characterization of a signal (3, 4). We sum the kurtosis values 

in different bands and use them as features.

f. Discrete Wavelet Transforms (DWT). Wavelet transforms employ a waveform shape that 

is better matched to a transient, impulsive response that is typical of these types of defects

where periodic impacts occur. Using wavelet decomposition, the signal was evaluated at 

different decomposition levels to establish the level that provides the best correlation to the 

damage level. In this case, level 9 decomposition was performed, and the energy at each 

level was calculated. The energy at approximation and detail filters were normalized as a

percentage of total signal energy to produce the features.
8

Table 3. Extracted features and methodology.

Signal Processing 

Method Name Description # of Extracted Features 

Time Domain

Statistics 

Calculate RMS, Kurtosis, 

Peak to Peak (P2P), Crest 

Factor, and Variance

40

(10 each signal)

FFT Band Energy Calculate energy in 

Frequency Spectrum in 

bands of 4kHz 

40

(10 each signal)

FFT Bearing Fault 

Frequency Energy 

Calculate amplitude 

information at bearing 

fault frequencies 

48

(12 each signal)

Bearing Envelope 

Analysis 

Demodulates band pass 

filtered signal to better 

extract information at 

bearing fault peaks 

100

(25 each signal)

Spectral Kurtosis Kurtosis value in different 

frequency bands 

20

(5 each signal)

Discrete Wavelet 

Tranform

Calculates energy at 

approximation and details 

as % of total signal 

energy 

40

(10 each signal)

5.2 Feature Selection

5.2.1 For Health Assessment

To select the most useful features for health assessment, correlation of individual features with 

fault level was performed and ranked. The top five features were used for the health assessment 

algorithm. For reference, the top 20 features for each fault type are provided in table 4, where x1,y-1,z-1 and x-2 in the feature name identify which accelerometer (1 or 2) and direction the 

signal is associated with. Note that the top features come from nearly all of the feature extraction 

techniques, emphasizing the value in employing a variety of techniques.
9

Table 4. Top features for health assessment.

Inner Race Faults Outer Race Faults Ball Faults

'P2P LowPass y-1' 'Details Level 8 y-1' 'P2P LowPass y-1'

'RMS LowPass y-1' 'P2P x-1' 'Crest Factor LowPass y-1'

'P2P z-1' 'Details Level 9 y-1' 'Crest Factor x-1'

'P2P y-1'

'y-1 FFT Band Energy from 

24001 Hz to 28000 Hz' 'Crest Factor LowPass z-1'

'SpecKurtBand5 y-1'

'y-1 FFT Band Energy from 

20001 Hz to 24000 Hz' 'P2P x-2'

'SpecKurtBand5 x-2' 'Details Level 8 x-2' 'P2P LowPass z-1'

'P2P x-2'

'z-1 FFT Band Energy from 

20001Hz to 24000Hz' 'Details Level 7 x-2'

'z-1 FFT Band Energy from 

24001 Hz to 28000 Hz'

'x-2 FFT Band Energy from 

20001 Hz to 24000 Hz' 'Kurtosis LowPass z-1'

'Kurtosis x-2' 'RMS y-1'

'x-2 FFT Band Energy from 1

Hz to 4000 Hz'

'RMS z-1' 'Kurtosis x-1' 'Crest Factor x-2'

'Variance_LowPass y-1'

'y-1 FFT Band Energy from 1 Hz 

to 4000 Hz'

'x-2 FFT Band Energy from 

8001 Hz to 12000 Hz'

'SpecKurtBand5 x-1' 'P2P x-2' 'Kurtosis x-2'

'P2P x-1' 'RMS x-2' 'Crest Factor y-1'

'x-1 FFT Band Energy from 

32001 Hz to 36000 Hz' 'Kurtosis LowPass z-1' 'Kurtosis y-1'

'x-1 FFT Band Energy from 

28001 Hz to 32000 Hz' 'P2P Envelope z-1' 'P2P Envelope x-2'

'RMS LowPass z-1' 'P2P Envelope y-1' 'Kurtosis LowPass y-1'

'Crest Factor x-1' 'Details Level 5 y-1' 'SpecKurtBand1 x-2'

'P2P Envelope x-2'

'x-2 FFT Band Energy from 1 Hz 

to 4000 Hz' 'BSF_Energy y-1'

'RMS Envelope x-2' 'P2P LowPass z-1' 'Crest Factor LowPass x-2'

'RMS LowPass x-2' 'Details Level 9 x-2' 'BSF_Energy z-1'

5.2.2 For Fault Classification

For selecting appropriate features for fault classification, a wrapper method was used. A Naïve 

Bayes classifier was used as the classification algorithm to which the wrapper feature selection 

technique was applied. Naïve Bayes classification will be described in more detail later in this 

report. In the wrapper method, a forward selection process is done, in which a new feature is 

added only if it provides noticeable improvement in the classification results. The output of the 

wrapper technique contains the best features to include in the classification analysis (5). The top 

features are listed in table 5.
10

Table 5. Top features for fault classification.

'RMS x-1'

'Kurtosis x-1'

'P2P LowPass x-1'

'Crest Factor y-1'

'Variance y-1'

'RMS LowPass y-1'

'Kurtosis LowPass y-1'

'Crest Factor z-1'

'Variance x-2'

'RMS LowPass x-2'

x-1 FFT Band Energy from 20001 Hz to 24000 Hz'

z-1 FFT Band Energy from 4001 Hz to 8000 Hz'

'x-2 FFT Band Energy from 36001 Hz to 40000 Hz'

'Details Level 1 x-1'

'Details Level 7 y-1'

'Details Level 4 y-1'

'Details Level 2_y-1'

'SpecKurtBand5 x-1'

5.3 Health Assessment

A Self Organizing Map (SOM) algorithm was used to assess the health of the bearings. This 

method was selected because it only requires baseline (healthy) data for training the algorithm. 

The main concept is that the algorithm clusters good data, and as data sets from bearings in 

question come in, their health is calculated based on proximity of their features to the features of 

healthy bearings. More specifically, the algorithm calculates a health value for each bearing

based on the Euclidian distance from the healthy training data based on multiple features (6, 7). 

The algorithm was trained with the top features from half of the healthy bearings. The features 

from the remaining healthy bearings and from the fault cases were used to evaluate the 

performance of the algorithm since the actual states of health were known.

5.4 Fault Classification

A Naïve Bayes classifier was used to identify the fault types of individual bearings. The 

technique is based on Bayes law, which relates the probability of an event (fault type) occurring 

on evidence (the vibration features). The method uses training data to determine the probabilities 

of having features in a certain range given the bearing fault type (including no fault/healthy). The 

classifier was trained with healthy data sets and all representative fault types. The trained model 

is then used to calculate the probability of a bearing under investigation of being in each class

(fault type), given the feature values. It then simply chooses the most probable fault type. For 

continuous values, such as the vibration features, one may assume a distribution for the features; 

in our case, we assumed a Gaussian distribution. Effectively, this means that for each fault type, 

a mean and standard deviation were estimated and a Gaussian distribution was fit using the 

training data. When a test sample comes in, the Gaussian distribution is used to estimate the 
11

probability of each fault type given that feature value. Since there are multiple features, a product 

formulation is used by multiplying the probabilities together. The classifier performs this 

probability calculation for each fault type and selects the most probable type. Cross validation 

was performed to randomly split up the data set into a training set and a test set for evaluation 

purposes. Advantages of this technique are that it is very straightforward and simple to apply, it 

is intuitive, it can be applied for classification problems with multiple classes, it and works 

reasonably well. The disadvantage is that an assumption of independent features is not always 

met; consequently, more sophisticated methods might perform slightly better (although they 

could be prone to over-fitting). Also, some classification algorithms, such as support vector 

machines, are only designed for binary classification (good/bad) and are more cumbersome to 

use for multiple classes (healthy, inner, outer, ball, etc.).

6. Results

The results, in general, are quite good. Plots of health vs. damage level for the bearings are 

presented for the three different fault levels in figures 5–7, where a higher value on the vertical 

axis indicates reduced health. The results indicate that a damaged bearing can easily be 

distinguished from a healthy bearing for all fault types and all fault levels, and that the calculated 

health values have an upward trend with the level of damage. There are two points of concern to 

be noted with the health values. First, there is a significant amount of scatter in many of the 

health values, particularly with the ball faults. Second, the health value for fault level 3 is better 

than for fault level 2 on the inner race fault plot (figure 6). Both of these items will be addressed 

in the Discussion and Conclusions Section. The results indicate that classification of the fault 

type is for the most part very good, with correct classification of healthy, inner race, and outer 

race faults 100% of the time and correct classification of ball faults approximately 75% of the 

time. The fault classification results are presented in table 6 as a confusion matrix, as is common.

The table presents the “predicted” classification of each of the runs of a given fault type (50 

healthy files and 50 files for each fault type). For example, 13 of the ball fault cases were 

misclassified as inner race faults and 37 of the ball fault cases were correctly classified. The ball 

faults present poorer results for both damage level vs. calculated health and fault type 

classification. This is not unexpected due to the high degree of randomness in which the damage

on the ball makes contact with the other components, coupled with the short duration of the 

impact when contact is made.
12

Figure 5. Bearing health value as a function of the outer race damage level.
13

Figure 6. Bearing health value as a function of the inner race damage level.
14

Figure 7. Bearing health value as a function of the ball damage level.

Table 6. Classification of fault types (confusion matrix).

Predicted

Healthy Inner Outer Ball

Actual

Healthy 50 0 0 0

Inner 0 50 0 0

Outer 0 0 50 0

Ball 0 13 0 37

7. Discussion and Conclusions

Algorithms have been developed that may be used to predict the health and fault type of a 

monitored bearing. Although the algorithms focus on inner race, outer race, and ball faults, they 

could be easily adapted to additional fault types. The algorithm for fault type classification works 

very well with some exception to ball faults. Misclassification of about 25% of the ball faults is 

almost certainly due to the randomness in which how and when the damage on the ball strikes 

the races. It is recommended that an improved set of features be examined for this application. 

Also, some code development work needs to be done to eliminate some randomness in the 

selection of the top features, which has been seen to have an effect on the results. The health 
15

assessment algorithm can be used to assess the health of a bearing. It always correctly shows 

whether or not there is some level of damage. The confidence in the health value for certain fault 

types and fault levels is questionable. The issue is in variability of the feature values at those 

particular fault levels/types and hence health assessment values. Use of a larger data set should 

greatly improve the problem. One curious item is that fault level 3 appears to be healthier than 

fault level 2 for the inner race. It is believed that this may very well be the case as many of the 

features were examined and all appear to provide the same result. Unfortunately, the engineer 

who produced the faults is no longer available for discussion of the issue. It may simply be a 

matter of mislabeling. It is significant to note that the top features for both health assessment and 

fault classification come from nearly all of the feature extraction techniques, emphasizing the 

value in incorporating a variety of techniques. Finally, it is believed that significant 

improvements could be attained by enhancements in processing techniques, incorporating 

additional features, and tuning and optimizing the code.
16

8. References

1. Bayba, A.; Washington, D.; Tom, K. Seeded Fault Bearing Experiments: Methodology and 

Data Acquisition; ARL-TR-5575; U.S. Army Research Laboratory: Adelphi, MD, 2011.

2. McFadden, P. D.; Smith, J. D. Vibration Monitoring of Rolling Element Bearings by High 

Frequency Resonance Technique. A Review, Tribology International 1984, 77, 3–10.

3. Antoni, J. The Spectral Kurtosis: A Useful Tool for Characterizing Nonstationary Signals. 

Mechanical Systems and Signal Processing 2006, 20, 282–307.

4. Antoni, J.; Randall R. B. The Spectral Kurtosis: Application to the Vibratory Surveillance 

and Diagnostics of Rotating Machines. Mechanical Systems and Signal Processing 2006, 20, 

308–331.

5. Guyon, I.; Elisseeff, A. An Introduction to Variable and Feature Selection. The Journal of 

Machine Learning Research 2003, 3, 1157–1182.

6. Kohonen T.; Oja E., Simula O.; Visa, A.; Kangas J. Engineering Application of the SelfOrganizing Map. Proceedings of the IEEE 84 (10) pp. 1358–1384, 1996.

7. Qiu, H.; Lee, J.; Ling, J.; Yu, G. Robust Performance Degradation Assessment Method for 

Enhanced Rolling Element Bearings Prognostics. Journal of Advanced Engineering 

Informatics 2003, 17, 127–140.
17

List of Symbols, Abbreviations, and Acronyms

ARL U.S. Army Research Laboratory

BD ball diameter

BPFI ball pass frequency inner

DWT Discrete Wavelet Transforms

PD pitch diameter

P&D prognostics and diagnostics

RMS root mean square

RUL remaining useful life

SOM Self Organizing Map
18

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BLDG 4603

APG MD 21005

NO. OF

COPIES ORGANIZATION

1 NASA GLENN

(PDF US ARMY RESEARCH LAB

ONLY) ATTN RDRL VTP H DECKER

BLDG 23 RM W121

CLEVELAND OH 44135-3191

3 US ARMY TARDEC

(PDF ATTN RDTA RS C BECK

ONLY) ATTN RDTA RS K FISHER

ATTN RDTA RS S HUSSAIN

MS# 204

6501 E 11 MILE RD

WARREN MI 48397-5000

1 USAMSAA

(PDF ATTN T. S. KILBY

ONLY) 392 HOPKINS RD

APG MD 21005