NASA / TM--2001-210943 IAF-00-J.5.06

Monitoring the Microgravity Environment

Quality On-Board the International Space

Station Using Soft Computing Techniques

Part I: System Design

Kenol Jules

Glenn Research Center, Cleveland, Ohio

Paul P. Lin

Cleveland State University, Cleveland, Ohio

Prepared for the

51st International Astronautical Congress

sponsored by the American Institute of Aeronautics and Astronautics

Rio de Janeiro, Brazil, October 2-6, 2000

National Aeronautics and

Space Administration

Glenn Research Center

June 2001

https://ntrs.nasa.gov/search.jsp?R=20010069674 2018-05-14T14:47:28+00:00Z
Acknowledgments

The authors would like to thank the entire PIMS team (past and present) who has contributed so much to the

advancement and understanding of the rnicrogravity environment characterization field. We gratefully

acknowledge Timothy A. Reckart, of ZIN Technologies, who did the graphics for this paper.

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Monitoring the Microgravity Environment Quality On-Board the

International Space Station Using Soft Computing Techniques

Part h System Design

KENOL JULES

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

PAUL P. LIN

Cleveland State University

Cleveland, Ohio 44115

ABSTRACT

This paper presents an artificial intelligence

monitoring system developed by the NASA Glenn

Principal Investigator Microgravity Services

project to help the principal investigator teams

identify the primary vibratory disturbance sources

that are active, at any moment in time, on-board

the International Space Station, which might

impact the microgravity environment their

experiments are exposed to. From the Principal

Investigator Microgravity Services' web site, the

principal investigator teams can monitor via a

graphical display, in near real time, which event

(s) is/are on, such as crew activities, pumps, fans,

centrifuges, compressor, crew exercise, platform

structural modes, etc., and decide whether or not

to run their experiments based on the acceleration

environment associated with a specific event.

This monitoring system is focused primarily on

detecting the vibratory disturbance sources, but

could be used as well to detect some of the

transient disturbance sources, depending on the

events duration. The system has built-in capability

to detect both known and unknown vibratory

disturbance sources. Several soft computing

techniques such as Kohonen's Self-Organizing

Feature Map, Learning Vector Quantization,

Back-Propagation Neural Networks, and Fuzzy

Logic were used to design the system.

INTRODUCTION

With the International Space Station (ISS) soon to

be operational, many of the scientific experiments,

which used to be conducted on-board the NASA

Space Shuttle orbiters for long duration

microgravity conditions will be conducted onboard the ISS, which will allow even longer

periods of microgravity. Many of these scientific

experiments will require knowledge of the

microgravity environment for accurate analysis

of the experimental data. The Microgravity

Measurement and Analysis Project (MMAP) at

NASA Glenn Research Center (GRC), which the

Principal Investigator Microgravity Services

(PIMS) project is a part of, has the responsibility

for measuring, analyzing, and characterizing the

microgravity environment for Principal

Investigator (PI) teams and providing expertise

in microgravity environment assessment.

The PIMS project at the NASA Glenn Research

center supports NASA's Microgravity Research

Division Principal Investigators (PIs) by providing

acceleration data analysis and interpretation for a

variety of microgravity carriers such as the Space

Shuttle, parabolic aircraft (KC-135), sounding

rocket, drop towers, the Russian Mir Space

Station, and the International Space Station (ISS).

In general, the PIMS project's acceleration data

support efforts are to archive and disseminate

accelerometer data; to support users interested in

the microgravity acceleration environment by

NASA/TM--2001-210943 1
providinginformationaboutactivitiesand

accelerationsources;toidentifyacceleration

sourcesrelatedto vehiclesystems,experiment

hardware,vibrationisolationsystems,andother

systems;todevelopdataanalysistechniques

anddisplaysperuserrequirements; toeducate

usersabouttheenvironmentanddataanalysis

techniques; toprovidestandarddatainterpretation

reports;andto characterizethemicrogravity

environmentof theISSin supportofPIs.

PIMShascharacterized the microgravity

environment for various earth orbiting platforms

as well as ground based facilities in support of

Pls from various science disciplines such as

biotechnology, combustion science, fluid physics,

materials science, and fundamental physics.

With the advent of the ISS operation, a vast

amount of acceleration data is expected to be

collected, processed, and analyzed for both the

ISS microgravity environment characterization

(verification) and scientific experiments. This

offers a unique challenge for the task of data

analysis. A comprehensive means of examining

the collected data to assist in identifying

significant acceleration trends and events is

needed. To tackle that problem, the NASA Glenn

PIMS project is currently developing an artificial

intelligence monitoring system, which will show

the principal investigator teams in near real time

any change in the microgravity environment that

might affect their experiments. This Artificial

Intelligence (AI) monitoring system will extract,

analyze, and interpret the most salient features

of the microgravity environment on-board the

ISS at any moment, in near real time, as data is

downlinked from the ISS for processing. Such a

system will help the Principal Investigator (PI)

teams monitor the microgravity environment

on-board the ISS in order to avoid, whenever

possible, any negative impact on their

experiments.

The PIMS ISS Microgravity Environment

Monitoring System (MEMS) will do the

following: 1) detect the current vibratory events

on-board the ISS in near real time; 2) classify each

known event and assess their relative impact on

the environment; 3) identify unknown events,

which require characterization. The system will

act as the expert eyes for the PIs, thus freeing

them from the burden of being a microgravity

environment analyst expert so that they can

concentrate on running / analyzing their

experiments. It is important to note that the

MEMS' main focus is the vibratory, regime, but

some of the transient activities could be detected

as well, depending on the events duration.

MICROGRAVITY ENVIRONMENT

The microgravity acceleration environment of an

orbiting spacecraft in a low earth orbit is a very

complex phenomenon. Many factors 1,such as

experiment operation, life-support systems,

equipment operation, crew activities, aerodynamic

drag, gravity gradient, rotational effects as well

as the vehicle structural resonance frequencies

(structural modes) contribute to form the overall

microgravity environment. The microgravity

acceleration environment, in general, can be

considered as made up of three components:

quasi-steady, vibratory, and transient components.

Quasi-steady

The quasi-steady component" is composed of

those accelerations whose frequency is less than

the lowest natural structural frequency of the

vehicle. Those accelerations vary over long

periods of time, typically longer than a minute.

This lowest natural structural frequency depends

on the vehicle. For the ISS, the lowest natural

structural frequency 3 is expected to be around

sO. 1 Hz. The system configuration and mass

properties, the specified altitude, and the attitude

control system are the principal contributors 4 to

the quasi-steady accelerations for the ISS.

Vibrator_

The vibratory component is composed of those

accelerations which are oscillatory in nature and

whose frequencies are greater than or equal to the

lowest natural structural frequency of the vehicle.

They are harmonic and periodic in nature with a

characteristic frequency. For the ISS, the

characteristic frequencies 4 of disturbance are

expected to be in the range of sub-Hertz to

NASA/TM--2001-210943 2
hundredsof Hertz(0.01- 300Hz).Thevibratory

componentincludesaccelerationscausedby

equipmentoperationsuchaspumps,fans,

communicationantennadithermotion,

centrifuges,andcompressors, crewactivity,

exercise,andstructuralmodeexcitations.

Transient

The transient component is composed of those

accelerations that last for a short period of time,

and are non-repetitive. The transient component 5

includes accelerations caused by the ISS thruster

operations, experiment operations, docking and

undocking, Intra-Vehicular Activity (IVA), ExtraVehicular Activity (EVA), and some other crew

actions.

Accelerometers

To measure and characterize the rnicrogravity

environment on-board the ISS, two primary

accelerometer 5.6 systems will be used: the Space

Acceleration Measurement System II (SAMS-II),

and the Microgravity Acceleration Measurement

System (MAMS). SAMS-II will measure the

vibratory and transient accelerations from 0.01-

400 Hz, and MAMS will measure the quasisteady, vibratory, and the transient accelerations

to verify the ISS microgravity environment

available to the users. Since SAMS II are

designated to measure and report the ISS

acceleration environment for experimenter teams'

use, the PIMS ISS-MEMS will use the

acceleration data measured by SAMS-II.

SYSTEM DESIGN STRUCTURE

Since the ISS will be operated in two modes, the

ISS MEMS is being developed to characterize

both: 1)Microgravity mode, and 2) Non-

. • • 4

microgravity mode. The specification states that

among the capabilities of the microgravity mode is

the ability of the ISS to provide the prescribed

acceleration environment at a minimum of 50% of

the internal user payload locations for 180 days

per year in continuous time intervals of at least

30 days, which will be interspersed with reboost

and docking events. The following events for the

microgravity mode will be characterized: vehicle

structural modes, crew exercise, experiment setup,

machinery start up, any long duration thruster

firing, and any unknown event, which falls within

the prescribed definition of vibratory disturbance

sources (Fig. 1). The non-microgravity mode can

last between 10 to 25 days after each 30 day

microgravity period. The following activities will

be characterized for this mode: altitude boost,

docking, undocking, EVA, IVA, and any unknown

event detected during this mode of operation

(Fig. 1).

SYSTEM OPERATIVE MODES

The ISS MEMS is setup to perform both on-line

and off-line processing. The on-line processing

(Fig. 2) consists of being able to detect all the

incoming events the system was trained to

recognize (see the soft computing techniques

description and PSD data description sections

below). In this mode, the system acquires the

acceleration data from the ISS downlink and

processes it to detect learned patterns. Once the

pattern is identified with a certain amount of

confidence, the reading is sent to the PIMS-ISS

MEMS web site, in near real time, so that all the

PIs can see the disturbance sources that might

impact the result of their experiments. At that

point, the PIs can decide either to continue

running the experiment or stop it, if possible.

In the off-line mode (Fig. 3), the system identifies

and verifies the patterns, which are unknown to

the on-line mode (patterns which have not yet

learned by the on-line mode). The on-line mode

acquires the pattern and then hands it over to the

off-line mode for further processing. This relieves

the on-line mode from doing any more guesswork,

but instead to focus on its main task, which is to

identify the learned patterns. Once the hand-over

is done, the off-line mode works in the

background. It stores certain features of the

unknown pattern such as frequency, acceleration

level, time and date the event was identified to a

database. The patterns stored in the database are

cross-referenced by a PIMS analyst with the ISS

data voice loop logbook from the NASA Glenn

Telescience Support Center (TSC) as well as with

acceleration data analysis to verify if, indeed, a

new event (an event which has not yet been

NASAfrM--2001-210943 3
characterized)id takeplace.If thedatavoice

looplogbookandaccelerationdataanalysis

concur,thatneweventislabeledandclassified

to matchitscharacteristicfrequency.Afterthe

accumulationof anumberofnewpatternsinthe

database, theISSMEMSisretrainedinorderto

recognizethenewpatternsaswellastheoldones.

SOFT COMPUTING TECHNIQUES

DESCRIPTION

At first, the problem to be solved sounds simple

enough: identify in near real time the active

vibratory disturbance sources, at any time, onboard the ISS from the acceleration data, which

are being downlinked from the station. However,

once the myriad of other factors are accounted for,

complexity arises and soon the problem becomes a

very complicated one to deal with. Many factors

must be taken into account such as multiple

sensors and science racks distributed throughout

the science module as well as vibratory

disturbance sources located throughout the ISS. In

addition, each sensor can be programmed to

operate at a different cutoff frequency, which

means that some disturbance sources will be

measured by more than one sensor (depending on

cutoff frequency). Due to the localized nature of

the vibratory component, sensor location must be

matched with rack location for this monitoring

system to be useful to PIs. Finally, not only that

the system must classify all the known patterns, in

near real time, but also it must recognize the

unknown ones as well since the ISS is a new

microgravity platform and is being built in

increments. As such, some events characteristic

frequencies will change from increment to

increment until ISS assembly complete. Thus,

when all of the factors mentioned above are taken

into account, the problem to be solved becomes a

complex and difficult one. As a result, several

techniques are being combined, each one to tackle

a specific aspect of the problem. The following

soft computing techniques are used: Kohonen's

Self-Organizing Feature Map, Learning Vector

Quantization, Neural Networks Back Propagation,

and Fuzzy Logic. A brief description of each of

the techniques used is given below.

Kohonen's Self-Organizing Feature Man

The principal goal 7 of the Self-Organizing Feature

Mapping (SOFM) algorithm, an unsupervised

learning technique, developed by Kohonen is to

transform an incoming signal pattern of arbitrary

dimension into a one- or two-dimensional discrete

map, and to perform this transformation

adaptively in a topological ordered fashion. The

essential ingredients of such an algorithm 7 are as

follows:

1. A one- or two-dimensional lattice of

neurons that computes simple

discriminant functions of inputs received

from an input of arbitrary dimension.

2. A mechanism that compares these

discriminant functions and selects the

neuron with the largest function value

3. An interactive network that activates the

selected neuron and its neighbors

simultaneously

4. An adaptive process that enables the

activated neurons to increase their

discriminant function values in relation to

the input signals

Since SOFM is intended only to visualize metrictopological relationships of input, it is not

recommended that it be used alone for pattern

classification and decision-making. Therefore, the

Learning Vector Quantization (LVQ) is used as

well.

Learning Vector Quantiz, ation

The Learning Vector Quantization 8 (LVQ) is a

supervised learning technique that uses class

information in order to improve the quality of the

classifier decision regions. The Learning Vector

Quantization algorithm is a stochastic

approximation algorithm. This algorithm is

designed to minimize the possibility of

misclassification since it learns to classify input

vectors into the target classes specified by the

user. More importantly, this algorithm 9 can be

trained to identify classes made up of multiple

unconnected regions.

NASA/TM--2001-210943 4
Neural Networks

Artificial neural networks 1°are created to mimic

the neural system in human brains. The network

consists of many interconnected nodes, similar to

neurons in the human brain. Each node assigns a

value (known as weight) to the input from each of

its counterparts. As these values (weights) are

changed, the networks adjust the way it responds.

A trained neural network can provide instant

mapping from input to output, which simulates the

complex behavior of any non-linear system. Since

the two techniques described above are used only

to classify known patterns, a Back-Propagation

Neural Network (BPNN) is used to identify

patterns, which are not yet known to the system.

Fuzzy Logic

Fuzzy logic _1is a mathematical technique for

understanding, and controlling specific

manipulation of continuously variable truthvalues. Fuzzy logic is all about the relative

importance of precision since it measures the truth

of a given situation as a matter of degree. Between

the input and the output, there is a black box that

does the work through the use of if-then rules,

which embody the knowledge that governs the

action of the system that is being described or

modeled. Fuzzy logic is used to handle multiple

sensors reading for the same event and matched

the reading with the racks location and vibrating

disturbance sources location (when known) to

determine which reading is more relevant to a

specific rack. The output of the fuzzy logic is the

degree of confidence of which sensor reading is

relevant to a specific experiment (rack).

COMBINATION OF THE FOUR

TECHNIQUES

SOFM, LVQ, and BPNN techniques are combined

into a single module, herein is referred to as

Adaptive Pattern Classification and Recognition

(APCR). APCR combines three neural networkbased techniques, which complement each other.

SOFM can group input vectors into several

clusters, but can not group clusters into class, nor

can it group different clusters into the same

desired target class like LVQ can. This feature

(different clusters into the same target class) is

very important for both the structural mode

excitation and crew exercise events. The structural

mode consists of several frequency components

spread over a frequency band. The crew exercise

has a similar signature with two distinct frequency

domain components (body rocking frequency and

leg pedaling or footfall frequency). In addition,

some vibrating sources exhibit harmonics of their

fundamental frequencies. In this case, all the

harmonics or the frequency band of a specific

event can be lumped into that event target class.

SOFM and LVQ can not be used to identify new

patterns (unlearned patterns), thus, BPNN is used

to detect the new patterns and store them in a

database. Finally, when multiple sensors detect the

same event, fuzzy logic is used to determine

which sensor reading is more relevant to a specific

experiment (rack) by using an index known as

degree of confidence, which varies from 0 to 1.

An index of 1 means that a specific experiment

might be impacted (if sensitive to it) by the event

that is being tracked by the sensor, while a reading

of 0 means no impact.

PSD DATA DESCRIPTION

The Power Spectral Density 12"13 (PSD) is a

frequency domain function, which is often used to

indicate the dominant frequency components

present in the data. PSD analysis is performed on

time series data to identify the relative magnitudes

of sinusoidal signals that compose the series. The

basis of this computation is the Fourier

transform 14,which indicates the magnitude of

each frequency (sinusoid) present in the time

history signal. PIMS analysts use the discrete

Fourier transform of a time series such that

Parseval's relation is satisfied: the RMS of a time

signal is equal to the square root of the integral of

the PSD across the frequency band represented by

the original signal. ISS MEMS uses the PSD data

to detect the characteristic frequency of each event

using a peak detector algorithm in order to classify

all incoming learned patterns (Fig. 4). All

unknown patterns are handed over to the off-line

mode of MEMS for further analysis.

NASA/TM--2001-210943 5
VISUAL DISPLAY VIA THE WEB

The PI teams will have access to MEMS via the

World Wide Web (WWW), where a graphical

display will show the status of all the vibratory

disturbance sources with their degree of

confidence. Both modes (microgravity and nonmicrogravity) will be displayed because even

when the ISS is in the microgravity mode, some

non-microgravity activities will occur, such as

docking, undocking. Figures 5 and 6 show the

typical events, which will be displayed for each

mode on the WWW. As new events are

characterized and added to MEMS, the display

will be updated to reflect that. From figures 5 and

6, one can see that each event is named after an

expected activity, for example, exercise. Also,

there is another label called "HEAD". Once an

activity is confirmed to be on (e.g., the x-axis for

exercise reads a confidence level of 80%), a PI

can click on the label "HEAD" to see where the

accelerometer reporting that activity is located. As

soon as the PI clicks on that label, a layout of the

science module is displayed, showing sensor and

rack location (Fig. 7). Obviously, if a sensor head

is located close to an experiment, which is

sensitive to crew exercise, for example, the PI of

said experiment should take appropriate action,

whenever possible.

The PIs can also click on any of the three axes

associated with a specific event to see a log for

that event. Once an event is detected, a log is

automatically created, which keeps useful

information such as the starting time, average

amplitude and frequency, event ending time (Fig.

8). Such a log will be very useful to PIs while

analyzing and interpreting their experimental data

since it gives them a simplified record of the

environment their experiments were exposed to.

Knowing the sensitivity of their experiments, they

can compare the activity log of an event with any

unexplained trend they might come across while

analyzing their data to see if such an event

correlates with the time segment they are

concerned with. If this log is used appropriately by

PIs, it has the potential to accomplish the same

function, which had been done in the past by a

microgravity environment characterization

analyst, albeit it should be used mainly for the

vibratory regime.

DISCUSSION AND CONCLUSION

Nihil simul inventum est et perfectum: nothing is

created and perfected at the same time. This work

does not escape that fate either. What is outlined

in this paper lays the foundation upon which the

PIMS project will build, improve and fine tune an

AI system capable to meet PIs requirements and

needs in near real time during ISS utilization.

MEMS is being developed to take into account

each ISS increment since the characteristic

frequency of some events, such as structural

modes, is expected to change as the ISS goes from

one increment to the next. The soft computing

techniques, which are combined for this work to

overcome the many technical challenges, at this

present moment, are the most suitable

combination to accomplish the technical

objectives outlined above. As new algorithms /

techniques emerge, they will be investigated to see

whether they can improve the system.

The main drawback of this system, as is the case

with any dynamic system, is the lag time between

when an event becomes active on-board the ISS

and when the system actually detects said event.

The routing of acceleration data from on-board the

ISS to PIMS Ground Support Equipment (GSE)

located at the NASA Glenn TSC causes such

transmission lag time. And once the acceleration

data is received, some data processing must be

done before any detection / classification can be

performed by the system. So, the challenge is how

the lag time can be minimized so that the system

prediction can become truly "real time". In the

near future, several options will be looked into in

order to lessen the impact of the lag time. One of

them is to process the data in the needed format

on-board the ISS before data downlink.

NASA/TM--2001-210943 6
1.

,

.

4.

.

6.

7.

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Measurements for MSL-I", NASA-TM 206979,

May 1998

NASA/TM--2001-210943 7
PIMS ISS Microgrevity

Env ronment Character zat on

I ISSNon-Microgravity Mode 1

ISS Microgravity

Mode

- Life support fans

- Pumps

- Freezer / Compressor

- Centrifuge

Figure 1. PIMS ISS-MEMS modes and events characterization flowchart

I AdspUve Pattern Classification and Recognition I

through SOFM, BPNN, LVQ

" i- ............... J

y__no

l------''°n through Fuzzy Logic r I °'_--r_ as Unknown 1

........ -;............................. V--

Figure 2. PIMS-ISS overall environment characterization flowchart

NASA/TM--2001-210943 8
I ISS _nllnk ,t

GRC -TSC _ ............................. t Console Monitoring t

J......... ;.....................................

I PlMS,SS-MEMS_ I Acceleration Da=to

l

l Adaptive Pattern

Classification and Recognltlon (APCR) _

Fuzzy logic

PIMS ISS-MEMS

Webeitl display

_I _'ES

Figure 3. PIMS ISS-MEMS On-line Processing flowchart

I Make use of ISS and 1

GRC-TSC voice loop log and

acceleration data m_lysla

NO YES

Keep uncllu_lad even!

in the unknown class

J for fu.he¢ analys.____J

Update the Orl-lthe i

Figure 4. PIMS ISS-MEMS Off-line Processing flowchart

NASA/TM--2001-210943 9
ON

OFF

ON

e

8

OFF

PIMS - ISS Environment Monitoring

Microgravity mode

ON ON ON

I

OFF

[ STRUCTURAL MODE [

ON ON .................

OFF OFF

x-axis y-sxls z-axis _-axls y-aXtS z-axis

Figure 5. Microgravity mode display

ON

Figure 6. Non-Microgravity mode display

NASA/TM--2001-210943 10
iAcceierati0nmeasuremen-g 

Systems Location _\

\

\

Figure 7. ISS Modules layout

il/i

Date : 5/28/00

Time : 00:00:000

Event Name : Exercise

Event Start Time : 00:00:000 :'-

Event Ends Time : 00:00:.000 _ ._. '_ -._:/;,_i_ :' ' ,.-

Event Ends Time :00:00:000

Event Afflpiitude t/_, :

Event Frequency [_, •

Time Event Reaches Maximum Amplitude :

Maximum Amplitude : /

Head : !

Axis : Z

Figure 8. Event log

NASA/TM_2001-210943 11
REPORT DOCUMENTATION PAGE FormApproved

OMB No. 0704-0188

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

June 2001 Technical Memorandum

4. TITLE AND SUST1TLE 5. FUNDING NUMBERS

Monitoring the Microgravity Environment Quality On-Board the International

Space Station Using Soft Computing Techniques

Part I: System Design WU- 101-46-0A-00

6. AUTHORISI

Kenol Jules and Paul P. Lin

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

8. PERFORMING ORGANIZATION

REPORT NUMBER

E-12797

10. SPONSORING/MONITORING

AGENCY REPORT NUMBER

NASA TM--2001-210943

IAF_J.5.06

11. SUPPLEMENTARY NOTES

Prepared for the 51 st International Astronautical Congress sponsored by the American Institute of Aeronautics and

Astronautics, Rio de Janeiro, Brazil, October 2-6, 2000. Kenoi Jules, NASA Glenn Research Center: and Paul P. Lin,

Cleveland State University. Stilwell Hall 232, Cleveland, Ohio 44115-2425. Responsible person, Kenol Jules, organization code 6727, 216-977-7016.

12a. DISTRIBUTION/AVAILABILITY STATEMENT

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Subject Category: 17 Distribution: Nonstandard

Available electronically at http://eltrs.grc.nasa.gov/GLTRS

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

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

This paper presents an artificial intelligence monitoring system developed by the NASA Glenn Principal Investigator

Microgravity Services project to help the principal investigator teams identify the primary vibratory disturbance sources

that are active, at any moment in time, on-board the International Space Station, which might impact the microgravity

environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the

principal investigator teams can monitor via a graphical display, in near real time, which event(s) is/are on, such as crew

activities, pumps, fans, centrifuges, compressor, crew exercise, platform structural modes, etc., and decide whether or not

to run their experiments based on the acceleration environment associated with a specific event. This monitoring system is

focused primarily on detecting the vibratory disturbance sources, but could be used as well to detect some of the transient

disturbance sources, depending on the events duration. The system has built-in capability to detect both known and

unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature

Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the

system.

14. SUBJECT TERMS

MEMS; PIMS; Artificial Intelligence (AI); ISS; Quasi-steady; Vibratory; SOFM;

Learning Vector Quantization (LVQ); Fuzzy Logic

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION

OF REPORT OF THIS PAGE

Unclassified Unclassified

NSN 7540-01-280-5500

19. SECURITY CLASSIFICATION

OF ABSTRACT

Unclassified

15. NUMBER OF PAGES

17

16. PRICE CODE

20. LIMITATION OF ABSTRACT

Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std. Z39-18

298-102