Kenol Jules

Glenn Research Center, Cleveland, Ohio

Paul P. Lin

Cleveland State University, Cleveland, Ohio

Daniel S. Weiss

Harvard University, Cambridge, Massachusetts

Monitoring the Microgravity Environment

Quality On-Board the International Space

Station Using Soft Computing Techniques

Part II: Preliminary System Performance Results

NASA/TM—2002-211813

August 2002

IAF–01–J.5.01
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Kenol Jules

Glenn Research Center, Cleveland, Ohio

Paul P. Lin

Cleveland State University, Cleveland, Ohio

Daniel S. Weiss

Harvard University, Cambridge, Massachusetts

Monitoring the Microgravity Environment

Quality On-Board the International Space

Station Using Soft Computing Techniques

Part II: Preliminary System Performance Results

NASA/TM—2002-211813

August 2002

National Aeronautics and

Space Administration

Glenn Research Center

Prepared for the

52nd International Astronautical Congress

cosponsored by the International Astronautical Federation, French Academy of Sciences,

Air & Space National Academy, Floral World Academy, and the Art & Literature Academy

Toulouse, France, October 1–5, 2001

IAF–01–J.5.01
Acknowledgments

The authors would like to acknowledge the contribution of Kenneth Hrovat, PIMS data analyst, ZIN Technologies,

for the knowledge and insights he provided during the development of this AI system. Also, many thanks to

Eugene Liberman (ZIN) for software interface development/modification and acceleration data processing for the

system training. Acknowledgment goes to Timothy A. Reckart (ZIN) as well, who did the graphics for this paper.

The authors gratefully acknowledge the unconditional support of David Francisco, NASA Glenn Research Center,

for the fostering and nurturing of AI work in the NASA Glenn Microgravity Measurement Analysis

Project (MMAP). Finally, we would like to thank Steve DelBasso, The Boeing Company, for providing us the

U.S. laboratory microgravity test results performed by Boeing at KSC in December 1999 and February 2000.

Available from

NASA Center for Aerospace Information

7121 Standard Drive

Hanover, MD 21076

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22100

Available electronically at http://gltrs.grc.nasa.gov
NASA/TM2002-211813 1 

Monitoring the Microgravity Environment Quality on-board the International Space 

Station Using Soft Computing Techniques 

Part II: Preliminary System Performance Results

Kenol Jules*

 National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Paul P. Lin†

Cleveland State University 

Cleveland, Ohio 44115 

Daniel S. Weiss‡ 

Harvard University 

Cambridge, Massachusetts 02138 

 

 

*

 Microgravity Research Scientist 

†

 Professor, Mechanical Engineering Department 

‡ Student 

ABSTRACT

This paper presents the preliminary performance results of the 

artificial intelligence monitoring system in full operational 

mode using near real time acceleration data downlinked from 

the International Space Station. Preliminary microgravity 

environment characterization analysis result for the 

International Space Station (Increment-2), using the 

monitoring system is presented. Also, comparison between 

the system predicted performance based on ground test data 

for the U.S. laboratory “Destiny” module and actual on-orbit 

performance, using measured acceleration data from the U.S. 

laboratory module of the International Space station is 

presented. Finally, preliminary on-orbit disturbance 

magnitude levels are presented for the Experiment of Physics 

of Colloids in Space, which are compared with on ground test 

data. The ground test data for the Experiment of Physics of 

Colloids in Space were acquired from the Microgravity 

Emission Laboratory, located at the NASA Glenn Research 

Center, Cleveland, Ohio. The artificial intelligence was 

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 of 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 

dynamic graphical display, implemented in Java, in near real 

time, which event (s) is /are on, such as crew activities, 

pumps, fans, centrifuges, compressor, crew exercise, 

structural modes, etc., and decide whether or not to run their 

experiments, whenever that is an option, based on the 

acceleration magnitude and frequency sensitivity associated 

with that experiment. 

This monitoring system detects primarily the vibratory 

disturbance sources. 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

The Principal Investigator Microgravity Services (PIMS) 

project at the NASA Glenn Research Center supports 

Principal Investigators of the microgravity science community 

throughout the period of the evaluation of the effects of onorbit acceleration on their experiments. The Principal 

Investigator Microgravity Services’ primary responsibility is 

to support NASA sponsored investigators in the area of 

acceleration data processing and analysis, interpretation and 

the monitoring of the reduced gravity environment on-board 

of various carriers. 

The residual acceleration environment of an orbiting 

spacecraft in a low earth orbit is a very complex phenomenon. 

It is subject to quasi-steady acceleration, higher frequency 

acceleration, and transient disturbances. Therefore, it is very 

important that Principal Investigator (PI) teams know what 

the environment was when their experiments were performed. 

Without that knowledge, they cannot and will not properly 
NASA/TM2002-211813 2

account for the effects of on-orbit acceleration disturbances, 

which are due to the environment of the space-laboratory 

instead of the phenomenon under study. 

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 reduced gravity environment. Weightlessness is an ideal state, which cannot be achieved in practice 

because of the various sources of acceleration present in an 

orbiting spacecraft. As a result, the environment in which 

experiments are conducted is not zero gravity; therefore, 

experiments can be affected by the residual acceleration 

because of their dependency on acceleration magnitude, 

frequency, orientation and duration. Therefore, experimenters 

must know what the environment was when their experiments 

were performed in order to analyze and correctly interpret the 

result of their experimental data. In a terrestrial laboratory, 

researchers are expected to know and record certain 

parameters such as pressure, temperature, humidity level and 

so on in their laboratory prior to, and possibly throughout 

their experiment. The same holds true in space, except that 

acceleration effects emerge as an important consideration. 

WHY AN ARTIFICIAL INTELLIGENCE 

MONITORING SYSTEM

With the International Space Station (ISS) currently 

operational (starting with flight 6A, which was launched on 

April 2001), a significant amount of acceleration data is being 

downlinked and processed daily on ground in an almost 

continuous basis for both the space station reduced gravity 

environment characterization (as well as vehicle verification) 

and scientific experiments. Therefore, to help principal 

investigator teams monitor the reduced gravity acceleration 

level on-board the ISS (and ease the PIMS’ data analyst daily 

task) in order to understand and avoid undesirable impact on 

their experiment, when possible, the NASA Glenn Principal 

Investigator Microgravity Services (PIMS) project developed 

an Artificial Intelligence (AI) monitoring system, which 

detects in near real time any change in the reduced gravity 

environment susceptible to affect PIs’ experiments. 

SYSTEM OBJECTIVE

The main objective of this monitoring system is to help the 

principal investigator teams, in near real time, identify the 

primary vibratory disturbance sources that are active at any 

instant of time on-board the ISS, which might impact the 

reduced gravity environment their experiments are exposed 

to. The soft computing techniques, which are used, consist of 

an adaptive pattern classification, which is a hybrid of 

Kohonen’s Self-Organizing Feature Map (SOFM), Learning 

Vector Quantization (LVQ), Back Propagation Neural 

Networks (BPNN), and fuzzy logic [2]. This monitoring 

system allows any principal investigator team, at any location 

and at any time, to see the current acceleration level, for the 

vibratory regimes, on-board the Space Station via the World 

Wide Web. From the Principal Investigator Microgravity 

Services’ web site, the principal investigator teams can see in 

near real time which event (s) is/are active, such as crew 

activity, pumps, fans, centrifuges, compressor, crew exercise, 

structural modes, reboost, extra-vehicular activity, etc., and 

decide whether or not to run their experiments based on the glevel associated with a specific event. A dynamic graphical 

display, implemented in Java, via the World Wide Web shows 

the status of all the vibratory disturbance sources with their 

degree of confidence as well as their impact on the reduced 

gravity environment. Part I of this paper, which was 

presented at the 51st International Astronautical Federation 

(IAF) Congress [3], in Brazil (2001), covered the system 

design. Part II focuses essentially on the performance and 

analysis capability of the system in full operational mode 

using live data downlinked from the Station. Preliminary 

reduced gravity environment characterization of the ISSIncrement-2 analysis result, using the AI monitoring system, 

is presented. Also, comparison between the system predicted 

performance and actual performance, using measured data onboard the station, is presented. Finally, preliminary on-orbit 

disturbance magnitude levels are presented for the 

Experiment of Physics of Colloids in Space (EXPPCS), 

which are compared with on ground test data. This 

monitoring system detects primarily vibratory disturbance 

sources. The system identifies both known and unknown 

vibratory disturbance sources to accommodate the 

incremental build-up nature of the Station. 

In summary, the Microgravity Environment Monitoring 

System (MEMS) performs the following tasks: 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 acts as the ISS microgravity 

environment analyzer for the PIs, thus freeing them from the 

burden of being a reduced gravity environment analyst so that 

they can concentrate on running / analyzing their experiments. 

It is important to note that the MEMS’ main focus is the 

vibratory regime. 

ACCELEROMETERS

To provide support for the science experiments, which require 

acceleration data measurement on the ISS, the NASA 

Physical Science Division sponsors two microgravity 

accelerometers; Space Acceleration Measurement System 

(SAMS) and Microgravity Acceleration Measurement System 

(MAMS). SAMS measures vibratory acceleration data in the 

range of 0.01 to 400 Hz for payloads requiring such 

measurement [4]. MAMS consists of two sensors. MAMSOARE Sensor Subsystem (OSS), a low frequency range 

sensor (DC to 1 Hz), is used to characterize the quasi-steady 

environment for payloads and the vehicle (ISS) and MAMSHigh Resolution Accelerometer Package (HiRAP) is used to 

characterize the ISS vibratory environment up to 100 Hz [5]. 

Both accelerometers were flown to the ISS on the Space 

Transportation System (STS-100), which was launched 

April 19, 2001, from the Kennedy Space Center (KSC). The 

preliminary results reported in this paper were obtained using 

both MAMS-HiRAP and SAMS accelerometer systems. The 

preliminary results presented covered the time period of 
NASA/TM2002-211813 3

May 11th to June 8th, 2001, [6] for which acceleration data 

were measured from the ISS using both systems. 

OVERALL SYSTEM DESCRIPTION

Due to the complexity associated with designing this system, 

three artificial neural networks techniques [7–10] were 

combined: SOFM, LVQ and BPNN (Fig. 1). In addition to 

these three techniques, fuzzy logic [11] was used to deal with 

the system fuzziness. For a complete description on how these 

techniques were used in the system design and integration see 

reference [3]. 

The system is designed to operate mainly in two modes: 1.) 

Microgravity mode, and 2.) Non-microgravity mode. An indepth description of the particular of these two modes relating 

to the ISS operation, can be found in references [12,13]. For 

reference 13, see Table 4. In brief, the microgravity mode, 

which is specified only for the ISS configuration at assembly 

complete, should last for 180 days per year in continuous time 

intervals of at least 30 days. During that time intervals certain 

operations or events, which are detrimental to the reduced 

gravity environment are not allowed. Those events are 

allowed only during the non-microgravity mode. The AI 

system captures both operation modes. The system is set up to 

perform both on-line and off-line processing. In the on-line 

processing mode, the system detects all incoming events it is 

trained for, while in the off-line mode, the system identifies 

all incoming unknown patterns, which the on-line mode have 

not yet trained for [3]. The system’s default operating mode is 

the dual mode (the system performs both tasks 

simultaneously). 

DYNAMIC GRAPHICAL DISPLAY

This section provides a brief overview of the dynamic 

graphical display module, which allows PIs to assess the 

reduced gravity environment level, in near real time. The 

module has two main components: a Graph Applet which 

displays the data and can be accessed by the users via the 

World Wide Web, and a Data Server which supplies data to 

the Graph Applet. The module is implemented in Java due to 

its portability and platform-independence. 

The Graph Applet

The Graph Applet is responsible for dynamically displaying 

the current ISS environment data. The Graph Applet is an 

extension of the Applet class, a native Java class that provides 

the functionality to embed the program within an hypertext 

mark-up language (HTML) page and access it via the World 

Wide Web. 

The Graph Applet has three main components: a drop-down 

menu that allows the user to select the sensor to be monitored, 

a panel used to display messages on the screen, and a set of 

bar graphs, each of which displays data for a single ISS event. 

Along with each bar graph are four buttons: sensor_id, x-axis, 

y-axis and z-axis (Figure 3). Clicking the sensor_id button 

for an event calls up a diagram showing where the 

accelerometer reporting on the event is located; clicking on an 

axis buttons shows a data log for that axis. 

The Data Server

Due to data transmission restrictions placed on Applets, they 

are only allowed to make network connections to the machine 

from which they are being served. This means that it is 

impossible for Graph Applet clients to connect directly to the 

Microgravity Environment Monitoring System (MEMS) data 

server, which provides the actual ISS vibratory disturbance 

data, instead they are connected to a Data Server program 

which runs on their host machine and acts as a pipeline to the 

MEMS data server. Therefore, they are two data server: an 

internal one and an external one. 

Configuration Data

In order to determine which events need to be displayed for 

each sensor, the Graph Applets consult a set of configuration 

files that store the frequency range for each sensor and event. 

An event is displayed for a given sensor if its frequency range 

overlaps the sensor’s frequency range by at least 75 percent. 

These configuration files can be changed on the fly. At any 

time the system administrator can use the External Server to 

issue a reload command to the Graph Applet clients that 

instructs them to use whatever new values have been stored in 

the configuration data files. 

 

Network Protocols

Communications between the MEMS data server and Internal 

Server, as well as between the Internal Server and External 

Server, use the transmission control protocol /internet 

protocol (TCP/IP). This protocol provides guaranteed packet 

delivery along these channels. However, TCP/IP is not well 

suited for communications between the External Server and 

Graph Applet clients because it requires a separate threaded 

socket for each connection, which would unduly tax system 

resources for large numbers of clients. Instead, these 

communications rely on the user datagram protocol (UDP) 

and use liveness countdowns and regularly generated packets 

from the clients to ensure that data is successfully propagated 

throughout the system. 

PSD DATA DESCRIPTION

The Power Spectral Density [14,15] (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 [16], which 

gives an estimate of the distribution of power with respect to 

frequency in the acceleration signal. PIMS analysts use the 

discrete Fourier transform of a time series such that Parseval’s 

relation is satisfied: the root means square (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. 
NASA/TM2002-211813 4

ISS MEMS uses the PSD data to detect the characteristic 

frequency of each event using a peak detector algorithm in 

order to identify all incoming learned patterns. All unknown 

patterns are handed over to the off-line mode of MEMS for 

further analysis (Fig. 2). 

POWER SPECTRAL DENSITY VERSUS TIME 

(SPECTROGRAM)

Color spectrograms provide a road map of how acceleration 

signals vary with respect to both time and frequency. To 

produce a spectrogram, PSDs are computed for successive 

intervals of time. The PSDs are oriented vertically on a page 

such that frequency increases from bottom to top. PSDs from 

successive time slices are aligned horizontally across the page 

such that time increases from left to right. Each timefrequency bin is imaged as a color corresponding to the base 

10 logarithm of the PSD magnitude at that time and 

frequency. Spectrograms are particularly useful for 

identifying structure and boundaries in time and frequency 

over relatively long periods of time [14,15]. All of the data 

(MAMS-HiRAP and SAMS) presented in this paper were 

obtained by inspecting visually many color spectrogram plots 

over a 10-day period to determine which strips of the 

spectrogram plots need to be analyzed for specific frequency 

range. 

DATA REDUCTION AND ANALYSIS

In order to perform the analysis, which generates the results 

presented below, many spectrograms were generated to 

qualitatively assess when a certain event took place during the 

10-day period for which acceleration data were collected 

aboard the ISS. Once the spectrograms were generated, visual 

inspections were made to select which specific time segments 

from the spectrograms PSDs should be generated for, from 

which the adaptive pattern recognition and classification 

(APRC) can extract the needed information to perform the 

identification and classification analysis. For a sampling rate 

of 1,000 samples per seconds, it took the software generating 

the PDSs 8 seconds using 8,193 points for a frequency 

spectrum of 0 to 100 Hz for each PSD. To reduce spectral 

variance, spectral averaging of 10 consecutive frames was 

used. Once the PSDs are generated, APRC begins with a peak 

detector algorithm, which detects all the major peaks of that 

PSD set. Each PSD set is made of x, y and z-axes. For the 10-

day period, a total of 3600 patterns for each axis were 

detected (a total of 10,800 for all three axes). A pattern, for 

example for the x-axis, contents the frequency detected along 

with the acceleration level for that event. The patterns for all 

three axes were identified and classified as unknown. 

The patterns were further classified as narrow or broad 

bands. Narrow band is defined as any detected frequency, 

whose deviation is no more than ± ∆f (one frequency 

resolution∆f = ± .122 Hz for this work) from its nominal 

frequency; broadband otherwise. For the broadband case, the 

median frequency as well as the lowest and the highest values 

are reported. For each acceleration level associated with a 

detected frequency, in both narrow and broad bands, the 

lowest as well as the highest acceleration values are reported. 

Once all the 10,800 patterns were classified, all the detected 

peaks verified with PSDs and spectrograms, patterns were 

sorted out and tables generated for all three axes as reported 

below. 

Microgravity vibration environment tests were conducted on 

the U.S. laboratory module (Destiny), at KSC during 

December 1999 and in February 2000. For this test the U.S. 

Lab module was configured in the stage 6A on-orbit 

configuration with all the eleven (11) system racks. All 

equipment racks were outfitted with their appropriate on-orbit 

equipment. The U.S. Lab system racks house 129 potential 

sources of disturbance. The largest four potential disturbers 

are: the pump package assembly (PPA) and the 

temperature/humidity control (THC) cabin air fan located in 

the rack # 6 on the port side; the PPA located in rack # 6 on 

the starboard; and the avionics air assembly (AAA) fan 

located in rack # 6 on the floor or deck. All system equipment 

such as pumps and fans, except the carbon dioxide removal 

assembly (CDRA), were operational. The international 

standard payload racks (ISPR) were not installed in the U.S. 

Lab during this test (thirteen locations are reserved for ISPR 

racks). For more information regarding the U.S. Lab KSC 

microgravity test, the readers should refer to [17–20]. 

The test data were obtained from the Boeing Company. The 

data were in the form of constant narrowband PSD and onethird octave band amplitude-frequency spectra. The sampling 

rate was fixed during data acquisition at 1024 samples per 

second. Both background and baseline noise measurements 

were taken. Background data usually refers to measurements 

that are due to the ambient building source. No U.S. Lab 

equipment was running during these measurements. Baseline 

refers to data taken before a lab measurement. Baseline noise 

was measured with both space station processing facility 

(SSPF) and the U.S. Lab external support equipment 

operating, but with all interior U.S. Lab equipment shut 

down. The U.S. Lab external support equipment includes the 

following: 2 chiller pumps on cooling servicer, large vacuum 

pump, power supplies, computers and 2 ground support 

equipment coolant circulation pumps (during some runs). For 

the data presented below regarding the U.S. Lab ground data, 

the baseline noise measurements were removed. 

DISCUSSION AND CONCLUSION

This paper presents some preliminary results, which assess the 

ISS microgravity environment, in order to share with the 

microgravity scientific community what is currently being 

observed in terms of disturbances aboard the ISS. It must be 

pointed out that the ISS microgravity environment 

characterization is at an early stage, therefore, not too much is 

known with certainty. To give the microgravity scientific 

community some ideas of the daunting task of characterizing 

such a complex platform, many sets of data are used, such as 

MAMS-HiRAP, SAMS, actual on-orbit payload (experiment) 

performance, which are compared with on ground test data in 

order to assess the on ground versus on-orbit performance 

deviation. More specifically, actual comparisons are made 

using the U.S. Laboratory “Destiny” ground test result, which 

was performed at Kennedy Space Center (KSC) in December 
NASA/TM2002-211813 5

1999 and February 2000, with actual on-orbit measured data. 

Also, EXPPCS on-orbit performance (how loud or quiet it is) 

is compared with ground test, which was performed in the 

Microgravity Emission Lab (MEL) on May 4–5, 2000, at 

NASA Glenn Research Center. The results presented here are 

VERY preliminary since we are in the early stage of receiving, 

processing, analyzing, digesting and comparing the data. 

There are more questions at this stage than answers. With that 

in mind, let’s discuss the significance of the results presented 

below in this paper. 

Table 1 shows both acceleration levels and frequencies for 

different activities recorded by MAMS-HiRAP aboard the 

ISS over a 10-day period. The table shows activities, which 

are observed only on the X, Y and Z-axes of the MAMSHiRAP, respectively. Then, activities observed on two axes, 

and finally, on all three axes. This is very important for 

experiments, which have directional and/or frequency 

sensitivities. It is very important to note that these data are 

presented in the MAMS-HiRAP on-orbit coordinate systems 

[21]. The table provides information such as the nature of the 

disturbance detected (narrow or broadband). In the case of the 

broadband, the frequency range (low and high end) is 

provided; also, the median acceleration level is provided 

along with the lowest and the highest level of acceleration 

detected for that specific activity over the 10-day period. 

Finally, a column, which lists some comments, provides some 

speculations on what that specific activity might be. We have 

reasons to believe that most of them are correct, but since this 

is a preliminary look at the data, we will call them 

“speculation” at this point, until more in-depth analysis is 

performed. 

Table 2 presents the same type of data, but for a different 

accelerometer, SAMS, for head 121f06, which was located in 

EXPRESS rack 2, mounted on the EXPPCS [22,23] test 

section. MAMS-HiRAP was located in EXPRESS rack 1, 

which is a non-isolated (meaning a non-Active Rack Isolation 

System (ARIS)) rack, while the SAMS sensor head used to 

collect the data presented in this table was located in an ARIS 

rack (rack 2). An ARIS rack is designed to attenuate the 

disturbance effects onboard the ISS within a specified 

frequency band (0.01 to 1 Hz or 2 Hz dependent on rack 

optimization configuration); they may be vehicle structural 

modes or other disturbances cause by experiments not located 

in an ARIS rack. Therefore, Table 2 is an eye-bird view of 

what an ARIS‡

 rack detects compared to a non-ARIS rack 

(Table 1). The detail explanation of the difference in the 

environment seen by these two racks is beyond the scope of 

this paper. Again, it must be pointed out that the data 

presented in Table 2 is in SAMS’ head 121f06 on-orbit 

coordinate systems [22], which is not the same with MAMSHiRAP. MAMS-HiRAP on-orbit +X-axis corresponds to 

SAMS’ 121f06 on-orbit -Z-axis, HiRAP -Y-axis corresponds 

to SAMS’ 121f06 -Y-axis and HiRAP -Z-axis corresponds to 

SAMS’ 121f06 -X-axis [21]. 

 

‡ Timeline information regarding which mode ARIS was in during 

the time period considered here was not available at the moment this 

paper was written. 

Table 3 presents the result of the U.S. Lab “Destiny” ground 

test performed at KSC. The result is the reduced data. 

Baseline noise measurements have been removed from the on 

ground measured data. Table 3 is provided as a comparison 

between what was measured on-ground and what is actually 

being detected on-orbit (Tables 1 and 2). Table 3 shows the 

signature (frequency) of the different equipments such as fans, 

pumps, and blower, located throughout the U.S. Laboratory. 

Notice that Table 3 is equipment and axis specific. The 

U.S.-ISS analysis coordinate system was used for the sensors 

orientation during the ground test of the U.S. module. The 

axes comparison between Table 3 (ground test) and Tables 1 

and 2 (MAMS-HiRAP and SAMS 121f06) are as follow: +XA

(ISS analysis X-axis) corresponds to +XH (HiRAP) and –Zf06

(SAMS); +YA corresponds to –YH and -Yf06; +ZA

corresponds to -ZH and –Xf06. 

Table 4 shows the actual acceleration level (on ground) when 

all the equipments aboard the U.S. laboratory were switched 

on except for one: the Carbon Dioxide Removal Assembly 

(CDRA) fan. The addition of the CDRA fan does not 

contribute much to the environment. Table 4 shows 

frequencies with associated acceleration levels for the 

microgravity disturbers in the U.S. Lab for X, Y and Z-axes, 

respectively. 

Figures 4–6 show the on-orbit versus on-ground disturbance 

levels of the EXPPCS payload (experiment) measured by 

MAMS-HiRAP and SAMS 121f06 (for both on-orbit data 

and on ground). The flight unit, with SAMS 121f06 head 

mounted exactly as the on-orbit configuration, was ground 

tested before flying to ISS. As was mentioned before, the 

ground data was taken from the microgravity emission lab 

(MEL) at NASA Glenn. The EXPPCS experiment contains 

eight different colloid samples. Each sample consists of a 

fluid and solid particle mixture. The mix/melt operation was 

performed many times on-orbit with the longest period of 

operation of 5 hours in a given day. Most mix/melt operations 

is on the order of 5 minutes with the initial sedimentation 

mixes to eliminate sedimentation taking up to 30 minutes 

each. The operation is performed to eliminate sedimentation 

and aggregation of particles and to disperse the solid particles 

throughout the fluid. This operation consists of quickly 

oscillating the sample cell via a DC motor and belt system 

[24]. 

From Figures 4–6, the plots labeled Mix/Melt and 

sedimentation are the on ground test data measured from the 

MEL for these two operation modes, while the SAMS 121f06 

and MAMS-HiRAP data were measured at the same time (onorbit) at two different locations on the ISS. The on-orbit data 

is mainly Mix/Melt operations (how much is sedimentation is 

not known since no information in that regard was available 

during that segment of on-orbit operation). The data measured 

by MAMS-HiRAP was up to 100 Hz (that is HiRAP cut off 

frequency). SAMS 121f06 head was mounted directly on the 

EXPPCS test section (the same configuration with the ground 

test in the MEL), while HiRAP was located approximately 

42.5 inches away from the SAMS 121f06 head. Note that a 

much higher magnitude level was measured on ground than 
NASA/TM2002-211813 6

on-orbit. Another significance of Figures 4–6 is that they 

show the on-orbit magnitude attenuation (or on-orbit transfer 

functions) since SAMS 121f06 and HiRAP measurements 

were taken from two different racks (the two sensors were 

located 42.5 inches). Note that Figure 5 shows only SAMS 

and MAMS-HiRAP on-orbit data. This is due to the fact that 

no Y-axis data was available for the MEL ground test. 

It is our hope that soon we will be able to identify most of the 

signatures listed in the tables presented in this paper. We hope 

that this paper will help shedding some lights on the 

complexity involved in characterizing the ISS and also the 

result presented will start a meaningful discussion within the 

microgravity scientific community as to how best to use the 

data to start removing some of the uncertainties associated 

with the prediction/simulation tools for the ISS. 

REFERENCES

1. Hamacher, H., Fluid Sciences and Materials Science in 

Space, Spring-Verlag, 1987. 

2. Lin, P.P., and Jules, K., “An intelligent System for 

Monitoring the Microgravity Environment Quality onboard the International Space Station,” IEEE 

Instrumentation and Measurement Technology 

Conference 2001, Vol. 3., pp. 2117–2122, Budapest. 

3. Jules, K., and Lin, P.P., “Monitoring the Microgravity 

Environment Quality On-board the International Space 

Station Using Soft Computing Techniques—Part I: 

System Design,” IAF-00-J.5.06, October 2000, Rio de 

Janeiro, Brazil. 

4. Space Acceleration Measurement System II, RTS 

SE Electronic Test Report, SE 121-Q02, SAMS-II 

657-RPT1, NASA, December 2000. 

 

5. “Microgravity Acceleration Measurement System 

 for the International Space Station,” Canopus 

 Systems Inc., CSI 0101 MAMS TR008, Contract 

 No: NAS3–26556, May 2001. 

6. PIMS on-console log book, May–June, NASA 

 Glenn Research Center, Cleveland, Ohio, 

 May–June 2001. 

 

7. Haykin, S., Neural Networks: 

 A Comprehensive Foundation, Prentice Hall, Inc., 

 1994. 

8. Kohonen, T., “Improved Versions of Learning 

 Vector Quantization,” International Joint 

 Conference on Neural Networks, V.1, pp. 545– 

 550, San Diego, 1990. 

9. Kohonen, T., “Learning Vector Quantization for Pattern 

Recognition,” Technical Report TKK-F-A601, Helsinki 

University of technology, Finland, 1986. 

10. Freeman, J.A., and Skapura, D.M., “Neural Network: 

Algorithms, Applications, and Programming 

Techniques,” Addison-Wesley Publishing Company, 

Inc., 1991. 

11. Jang, J.S., and Gulley, N., Application of Fuzzy

Logic in Control Design, Technical Brief, 

Mathworks Inc., 1996. 

12. Microgravity Analysis Integration Team, “Microgravity 

Control Plan (Revision B)”, Report No: SSP-50036B, 

The Boeing Company, Houston, TX, NASA—Contract 

No. NAS15–1000 (Drive-16), February 1999. 

13. International Space Station User’s Guide Release 2.0, 

NASA. 

14. Rogers, J.B. Melissa et al. “Analysis 

 Techniques for Residual Acceleration Data”, 

 NASA–TM 103507, July 1990. 

15. Rogers, J.B. Melissa., Hrovat, Kenneth et al. 

 “Accelerometer Data Analysis and Presentation 

 Techniques,” NASA–TM 113173, September 1997 

16. Moskowitz, E. Milton., Hrovat, Kenneth et al. 

 “Summary Report of Mission Acceleration 

 Measurements for MSL-1,” NASA–TM 206979, 

 May 1998. 

17. O. Crenwelge., “U.S. Lab Microgravity Test: Low 

Frequency Mode Resolution”, Boeing, SSP-MG00-034, 

April 25, 2000. 

18. E. O’keefe., V. Shekher., “ISS Lab Microgravity 

Vibration Environment Measurements and SEA Model 

Comparisons.” AIAA–2001–6406. 

19. O. Crenwelge., E. O’Keefe., “U.S. Lab Microgravity 

Test: Quick-Look Test and Data Report,” Boeing, SSPMG00-013, February 28, 2000. 

20. V. Shekher., “U.S. Lab Microgravity Disturbance 

Prediction Correlation with KSC Test Measurements, 

Revision 1,” V Memo #: 2-8V6C-VS-009/00, Rev 1, 

June 2, 2000. 

21. K. Jules., K. Hrovat., E. Kelly., “International 

Space Station Increment-2 Quick Look Report,” 

NASA/TM–2002-211200, January 2002. 

22. K. Jules et al. “International Space Station 

Increment-2 Microgravity Environment Summary 

Report,” NASA/TM2002-211335, January 2002. 

23. http://www.grc.nasa.gov/WWW/PAO/PAIS/fs12grc.htm 

or 

http://www.spaceref.com/iss/payloads/exppcs.html
NASA/TM2002-211813 7

24. M. Houston., A. McNelis., “Microgravity Emission Lab 

Reprot for the Physics of Colloids in Space (PCS) 

Experiment and Avionics Packages,” NASA-MEL-TR 

00-04, January 30, 2001. 

 

SOFM BPNN LVQ

YES NO

INPUT

VECTOR

Does

Pattern

fit prescribed

tolerance ?

Classify pattern

as unknown

OUTPUT

VECTOR

Database

Figure 1: Adaptive Pattern Recognition and Classification (APRC) 

PSD2 (X, Y, Z) PSD3 (X, Y, Z) PSDn PSD (X, Y, Z) 1 (X, Y, Z)

Adaptive Pattern Classification and Recognition

through SOFM, BPNN, LVQ

Source Detection (X, Y, Z)

PIMS-ISS Web display

Confidence Determination

through Fuzzy Logic

Stored in database for 

verification and training

• • •

Known Patterns ? yes no

Classify Patterns

as Unknown

Figure 2: MEMS-ISS overall environment Characterization Flowchart
NASA/TM2002-211813 8

Figure 3: Layout of the MEMS-ISS Web Dynamic Graphical Display 

GraphApplet 

x y z 

Event 1 

Sensor_id 

x y z 

Event 2 

Sensor_id 

x y z 

Event 3 

Sensor_id 

x y z 

Event 3 

Sensor_id 

x y z 

Event 4 

Sensor_id 

Message 

Graphs 

PIMS ISS: Environment Monitoring

sensor 1

Sensor List

GraphPanel 
NASA/TM2002-211813 9

Table 1: Frequencies Identification of the MEMS System for ISS Increment-2 over a 10-day period 

MAMS-HiRAP: X-axis only 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

1.8311 Narrow 27 8.4121 88.892 Structural 

3.7842 Narrow 65 6.9859 121.09 188.93 Structural 

4.6387 Narrow 50 21.582 111.43 Structural 

11.597 Narrow 20 7.1607 48.046 

13.672 Narrow 30 5.0939 69.260 

14.404 Narrow 51 17.729 84.30 

15.137 Narrow 59 16.879 84.536 

15.991 Narrow 28 15.855 72.29 162.14 

16.602 Narrow 30 5.9014 62.211 

18.677 18.818 19.531 Broadband 21 8.8645 37.908 

MAMS-HiRAP: Z-axis only 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

5.9814 5.8594 6.2256 Broadband 16 10.398 17.775 185.04 Structural 

7.5684 Narrow 8 5.2704 12.047 171.64 Structural 

MAMS-HiRAP: X and Z-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

0.97656 (x) 

0.97656 (z) 

 Narrow 

Narrow 

14.8 

33 

4.8229 

8.0831 

43.306 

65.824 

 Structural 

Structural 

39.185 (x) 

39.185 (z) 38.82 41.75 

Narrow 

Broadband 

9.0 

5.0 

5.7623 

3.2659 

15.071 

10.555 

 

MAMS-HiRAP: Y and Z-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

9.5215 (y) 

9.5215 (z) 

9.1553 

9.2773 

9.887 

9.644 

Broadband 

Broadband 

17 

30 

3.2905 

4.6486 

54.391 

87.878 

 

15.625 (y) 

15.625 (z) 

15.259 

15.015 

15.99 

15.99 

Broadband 

Broadband 

19 

36 

7.3364 

8.9874 

46.698 

74.023 

 

83.984 (y) 

83.984 (z) 

 Narrow 

Narrow 

30 

25 

7.5534 

21.413 

34.195 

34.03 

 EXPPCS 

EXPPCS 
NASA/TM2002-211813 10

Table 1: Concluded.

MAMS-HiRAP: X, Y and Z-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

.48828 (x) 

.48828 (y) 

.48828 (z) 

 Narrow 

Narrow 

Narrow 

8.50 

43.0 

40.0 

4.2772 

5.6869 

6.9941 

22.628 

108.69 

74.655 

180.59 

Structural 

Structural 

Structural 

1.3428 (x) 

1.3428 (y) 

1.3428 (z) 

Narrow 

Narrow 

Narrow 

16.0 

10.0 

30.0 

5.1388 

5.7588 

3.3875 

46.174 

20.517 

68.281 

 Structural 

Structural 

Structural 

2.5635 (x) 

2.5635 (y) 

2.5635 (z) 

 Narrow 

Narrow 

Narrow 

40.0 

17.5 

80.4 

8.6313 

3.9494 

55.104 

109.56 

34.825 

105.66 

195.83 Structural 

Structural 

Structural 

6.4697 (x) 

6.4697 (y) 

6.4697 (z) 

5.127 

5.9814 

6.3477 

7.446 

6.854 

6.958 

Broadband 

Broadband 

Broadband 

124.0 

21.0 

16.0 

13.372 

8.3173 

5.1989 

284.35 

56.712 

29.329 

 Structural 

Structural 

Structural 

7.8125 (x) 

7.8125 (y) 

7.8125 (z) 

 Narrow 

Narrow 

Narrow 

199.0 

45.0 

40.0 

14.995 

8.9089 

5.736 

401.54 

86.141 

91.883 

 

10.376 (x) 

10.376 (y) 

10.376 (z) 

 Narrow 

Narrow 

Narrow 

29.0 

60.0 

54.0 

5.6605 

18.339 

4.316 

56.275 

98.514 

117.39 

 

11.353 (x) 

11.353 (y) 

11.353 (z) 

 Narrow 

Narrow 

Narrow 

10.5 

16.9 

58.0 

4.1704 

12.896 

8.5245 

47.326 

20.916 

72.184 

 

11.963 (x) 

11.963 (y) 

11.963 (z) 

 Narrow 

Narrow 

Narrow 

27.0 

12.6 

28.0 

20.233 

3.8082 

6.7884 

50.008 

24.39 

49.333 

 EXPPCS 

EXPPCS 

EXPPCS 

13.184 (x) 

13.184 (y) 

13.184 (z) 

12.695 

12.695 

12.695 

13.428 

13.92 

13.92 

Broadband 

Broadband 

Broadband 

12.0 

33.0 

50.0 

4.7207 

14.753 

18.984 

29.494 

67.404 

85.7 

 

20.508 (x) 

20.508 (y) 

20.508 (z) 

19.653 

19.775 

19.653 

21.12 

21.36 

20.63 

Broadband 

Broadband 

Broadband 

13.0 

28.0 

28.0 

4.1738 

12.098 

8.8437 

31.446 

53.542 

60.844 

 

23.438 (x) 

23.438 (y) 

23.438 (z) 

 Narrow 

Narrow 

Narrow 

15.0 

32.0 

24.0 

5.4083 

15.789 

12.60 

27.038 

64.198 

54.73 

 SKV-1 

SKV-1 

SKV-1 

26.367 (x) 

26.367 (y) 

26.367 (z) 

25.879 

25.391 

25.391 

27.47 

27.34 

27.222 

Broadband 

Broadband 

Broadband 

6.0 

42.0 

23.0 

4.1576 

6.2566 

6.4870 

13.496 

100.07 

45.569 

 

49.438 (x) 

49.438 (y) 

49.438 (z) 

 Narrow 

Narrow 

Narrow 

10.0 

43.0 

16.0 

8.1438 

35.041 

12.400 

15.896 

51.334 

18.216 

 

57.617 (x) 

57.617 (y) 

57.617 (z) 

 Narrow 

Narrow 

Narrow 

250.0 

519.0 

222.0 

161.3 

323.77 

136.83 

393.98 

711.12 

305.99 

 ADVASC 

ADVASC 

ADVASC 

61.401 (x) 

61.401 (y) 

61.401 (z) 

60.913 

60.425 

60.547 

61.401 

62.988 

62.378 

Broadband 

Broadband 

Broadband 

11.80 

29.0 

21.0 

7.3783 

15.709 

9.3832 

20.117 

59.83 

29.381 

 

67.749 (x) 

67.749 (y) 

67.749 (z) 

 Narrow 

Narrow 

Narrow 

98.0 

132.0 

51.0 

68.329 

114.42 

39.852 

129.25 

181.34 

74.975 

 

69.458 (x) 

69.458 (y) 

69.458 (z) 

68.359 

68.97 

68.848 

69.824 

69.702 

69.946 

Broadband 

Broadband 

Broadband 

195.0 

270.0 

66.0 

108.24 

166.62 

31.245 

334.13 

498.39 

125.52 

 

75.684 (x) 

75.684 (y) 

75.684 (z) 

74.585 

74.707 

74.829 

78.125 

77.026 

77.271 

Broadband 

Broadband 

Broadband 

353.0 

200.0 

329.0 

154.72 

92.32 

70.549 

854.39 

587.84 

508.64 

 ADVASC 

A DVASC 

ADVASC 

87.769 (x) 

87.769 (y) 

87.769 (z) 

87.036 

86.914 

86.914 

88.013 

88.013 

88.013 

Broadband 

Broadband 

Broadband 

270.0 

60.0 

67.0 

129.87 

41.699 

49.281 

445.75 

76.801 

93.112 

 ADVASC 

ADVASC 

ADVASC 

96.069 (x) 

96.069 (y) 

96.069 (z) 

 Narrow 

Narrow 

Narrow 

150.0 

30.4 

35.0 

18.838 

7.9412 

8.522 

290.15 

67.442 

89.861 

625.59 

148.02 

EXPPCS 

EXPPCS 

EXPPCS 
NASA/TM2002-211813 11

Table 2: Frequencies Identification of the MEMS System for ISS Increment-2 for SAMS-121f06 

SAMS-121f06: X-axis only 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

1.2207 Narrow 14.276 12.848 15.704 Structural 

6.958 Narrow 22.60 20.34 24.86 

97.00 97.29 97.92 Broadband 37.051 23.591 50.51 EXPPCS ? 

SAMS-121f06: Y-axis only 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

0.48828 Narrow 49.498 18.837 80.16 Structural 

4.3945 4.1504 4.6387 Broadband 33.181 27.644 38.718 

7.0801 Narrow 19.602 17.642 21.562 

7.9345 Narrow 18.936 14.313 23.559 

9.3994 Narrow 14.527 13.074 15.979 

10.62 Narrow 10.062 9.0558 11.068 

19.836 Narrow 2.431 2.1828 2.6791 

24.78 Narrow 5.7657 4.7662 6.7652 

95.337 Narrow 26.318 10.242 42.395 EXPPCS ? 

SAMS-121f06: Z-axis only 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

0.12207 Narrow 2.3896 2.1506 2.6283 Structural 

0.36621 Narrow 7.1522 6.4369 7.8674 Structural 

0.97656 Narrow 22.305 20.074 24.535 Structural 

1.709 Narrow 3.4192 3.0773 3.7611 Structural 

6.3476 Narrow 31.809 15.986 47.633 

7.8125 Narrow 18.431 6.4848 30.377 

18.500 Narrow 2.1915 1.9723 2.4106 

SAMS-121f06: X and Y-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

1.0986 (x) 

1.0986 (y) 

 Narrow 

Narrow 

63.033 

19.778 

56.728 

17.800 

69.336 

21.756 

 Structural 

Structural 

5.8594 (x) 

5.8594 (y) 

Narrow 

Narrow 

72.285 

55.202 

47.012 

37.463 

97.574 

72.947 

 

17.578 (x) 

17.578 (y) 

17.092 17.94 Broadband 

Narrow 

1.1125 

1.4052 

0.9293 

1.0265 

1.2958 

1.5457 

 

51.391 (x) 

51.391 (y) 

 Narrow 

Narrow 

2.0688 

11.284 

1.8619 

10.156 

2.2757 

12.412 

 

SAMS-121f06: X and Z-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

0.8545 (x) 

0.8545 (z) 

 Narrow 

Narrow 

20.251 

13.400 

18.226 

6.2693 

22.276 

20.531 

 Structural 

Structural 

1.3428 (x) 

1.3428 (z) 

Narrow 

Narrow 

21.374 

4.3815 

19.237 

3.9433 

23.511 

4.8196 

 Structural 

Structural 

2.6855 (x) 

2.6855 (z) 

 Narrow 

Narrow 

60.906 

26.684 

54.815 

22.619 

66.996 

30.749 

 

9.5215 (x) 

9.5215 (z) 

 Narrow 

Narrow 

8.5605 

28.756 

7.7044 

25.880 

9.4165 

31.632 

 

SAMS-121f06: Y and Z-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

76.294 (y) 

76.294 (z) 

 Narrow 

Narrow 

11.047 

10.736 

10.857 

9.7225 

11.238 

11.751 

 

93.000 (y) 

93.000 (z) 

92.895 

92.895 

93.75 

93.23 

Broadband 

Broadband 

58.252 

79.823 

55.994 

71.841 

60.511 

87.805 

 
NASA/TM2002-211813 12

Table 2: Concluded. 

SAMS-121f06: X, Y and Z-axes 

Nominal 

Frequency 

(Hz) 

Low 

end 

(Hz) 

High 

end 

(Hz) 

Band type Median 

Level 

(μg) 

Low 

end 

(μg) 

High 

end 

(μg) 

Excitation 

level (μg) 

Comment 

2.3193 (x) 

2.3193 (y) 

2.3193 (z) 

2.3193 2.4414 

Narrow 

Broadband 

Narrow 

86.328 

40.206 

166.13 

77.695 

23.171 

149.52 

94.961 

57.241 

182.74 

Structural 

Structural 

Structural 

11.597 (x) 

11.597 (y) 

11.597 (z) 

11.475 

11.475 

11.719 

11.963 

Narrow 

Broadband 

Broadband 

9.517 

35.328 

27.507 

7.0646 

20.133 

17.242 

11.969 

50.523 

37.772 

 

23.437 (x) 

23.437 (y) 

23.437 (z) 

 Narrow 

Narrow 

Narrow 

0.9508 

4.2956 

2.3649 

0.8210 

3.7491 

1.1736 

1.0805 

4.8422 

3.5562 

 SKV-1 

SKV-1 

SKV-1 

26.489 (x) 

26.489 (y) 

26.489 (z) 24.780 26.850 

Narrow 

Narrow 

Broadband 

1.3839 

5.2389 

1.7697 

1.2455 

4.7150 

1.5927 

1.5229 

5.7628 

1.9467 

46.265 (x) 

46.265 (y) 

46.265 (z) 

45.776 

46.02 

46.446 

46.300 

Narrow 

Broadband 

Broadband 

32.336 

11.272 

9.3771 

28.729 

9.7991 

7.3253 

35.944 

12.744 

11.429 

 

54.321 (x) 

54.321 (y) 

54.321 (z) 

Narrow 

Narrow 

Narrow 

3.0549 

11.678 

6.2332 

2.0538 

10.510 

5.8437 

4.056 

12.846 

6.6228 

56.03 (x) 

56.03 (y) 

56.03 (z) 

 Narrow 

Narrow 

Narrow 

4.0949 

14.236 

26.485 

3.6854 

12.236 

24.580 

4.5044 

15.659 

28.391 

 

57.739 (x) 

57.739 (y) 

57.739 (z) 

Narrow 

Narrow 

Narrow 

6.6837 

33.219 

29.649 

6.0153 

26.313 

26.867 

7.3521 

40.125 

32.612 

 

59.204 (x) 

59.204 (y) 

59.204 (z) 

 Narrow 

Narrow 

Narrow 

3.7446 

22.784 

27.085 

3.3701 

22.177 

26.479 

4.1191 

23.391 

27.691 

 

61.401 (x) 

61.401 (y) 

61.401(z) 

 Narrow 

Narrow 

Narrow 

5.3333 

20.036 

13.404 

2.8005 

18.097 

10.870 

7.8662 

21.975 

15.938 

 

69.582 (x) 

69.582 (y) 

69.582 (z) 

69.214 

69.421 

69.985 

70.434 

Broadband 

Narrow 

Broadband 

4.4686 

9.8242 

7.3622 

4.0217 

8.8418 

6.2759 

4.9155 

10.807 

8.4486 

 

81.665 (x) 

81.665 (y) 

81.665 (z) 

 Narrow 

Narrow 

Narrow 

26.589 

25.335 

42.363 

24.831 

22.105 

32.576 

28.348 

28.565 

52.151 

 

85.937 (x) 

85.937 (y) 

85.937 (z) 

 Narrow 

Narrow 

Narrow 

6.0024 

11.105 

32.258 

5.4022 

9.9945 

26.359 

6.6026 

12.215 

38.157 

 

99.487 (x) 

99.487 (y) 

99.487 (z) 

99.400 

99.400 

99.853 

99.731 

Narrow 

Broadband 

Broadband 

37.051 

38.58 

80.248 

23.591 

37.121 

49.047 

50.51 

40.039 

111.45 

 
NASA/TM2002-211813 13

Table 3: U.S. Laboratory “Destiny” Microgravity Ground Test Single Disturbers Responses 

U.S. Lab: Pump Package Assembly (PPA) 

X-axis Y-axis Z-axis 

Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

41 103 41 121 64 66 

71 218 45.75 125 71 66 

140.25 1268 64.75 242 86.5 85 

280.5 2851 82.5 223 92.5 91 

 91.25 264 129 36 

 106 287 140.25 686 

 128.75 64 173 12 

 140.25 541 280.5 3104 

 280.5 9497 

U.S. Lab: Common Cabin Air Assembly (CCAA) Fan 

X-axis Y-axis Z-axis 

Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

40.75 80 40.75 30 41 39 

82.5 40 82.5 64 82.75 24 

103 1280 86 141 86.0 49 

190.75 147 95.25 543 95.25 168 

206.25 1306 103 1280 103 1235 

278.5 177 190.75 110 190.75 108 

 206 1306 206.25 1444 

 260.75 174 260.5 341 

 278.5 317 278.5 194 

U.S. Lab: Avionics Air Assembly ( AAA) Fan 

X-axis Y-axis Z-axis 

Freq 

uency (Hz) 

Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

29.5 73 29.5 40 29.5 21 

50.75 104 50.75 108 50.75 718 

61.25 59 61.25 78 61.25 153 

82.75 70 86.5 48 82.75 145 

86.5 42 101.5 40 86.5 85 

101.5 17 152.25 13 101.5 128 

120 22.9 173 25 152.25 13 

 193.75 50 165.75 24 

 225.25 28 173 12 

U.S. Lab: Carbon Dioxide Removal Assembly (CDRA) Blower 

X-axis Y-axis Z-axis 

Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

50.75 192 50.75 404 50.75 137 

101.5 49 101.5 218 101.5 17 

U.S. Lab: Trace Contaminant Control System (TCCS) Blower 

X-axis Y-axis Z-axis 

Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

40.75 173.61 46.75 201 50.25 139 

82.5 474 64 700 64 213 

127 393 86.75 555 86.75 240 

142 920 104.75 574 104.75 205 

284 1787 127 123 127.25 103 

 142 465 142 427 

 284 4957 284 2009 

U.S. Lab: Intermodule Ventilation System (IMV) Fan 

X-axis Y-axis Z-axis 

Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

20.75 17 29 15 29.5 55 

29.5 11 58.25 211 59.75 14 

41.25 14 69.25 35 71 6 

59.75 20 112.5 21 120 5 

84.5 5 138.75 23 141.5 12 

112.5 28 160.25 31 238 10 

119.75 13 225.25 13 277.5 19 

141.5 11 276.5 24 

160.25 13 

174.75 9 

225.25 9 

239.75 12 

247.5 34 

277.5 10 
NASA/TM2002-211813 14

Table 4: U.S. Laboratory “Destiny” Microgravity Ground Test, all sources active, except CDRA 

U.S. Lab: All Sources Operating, Except CDRA 

X-axis Y-axis Z-axis 

Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) Frequency (Hz) Acceleration (μg) 

40.75 176.71 39.0 133.44 46.5 82.751 

68.25 227.07 49.50 531.26 46.75 82.139 

68.75 226.39 57.0 417.01 47.25 83.225 

79.5 549.46 57.25 395.88 47.75 84.082 

87.0 205.29 58.0 448.20 48.0 83.609 

104.75 166.29 76.0 614.31 59.75 97.803 

122.25 186.25 76.75 603.14 76.00 218.98 

142.0 1728.4 94.50 269.7 76.75 212.87 

153.25 156.78 98.00 1028.6 77.0 201.53 

154.5 143.81 100.75 398.11 92.75 62.488 

156.25 130.75 112.75 118.31 93.0 64.414 

168.75 316.09 115.50 172.76 116.75 40.698 

237.25 658.11 142.0 1035.60 117.0 42.055 

254.50 594.24 166.50 690.38 117.75 44.657 

256.75 599.42 168.75 747.36 132.0 93.234 

269.75 656.03 170.0 851.49 132.25 98.548 

284.0 6756.3 171.75 872.35 132.75 102.93 

298.25 482.83 195.0 190.60 142.0 686.08 

 204.25 242.00 170.0 112.15 

 224.25 179.24 171.5 118.16 

 224.50 177.73 179.25 52.031 

 225.0 174.32 180.5 50.413 

 242.5 147.79 218.50 158.14 

 244.75 174.61 239.75 356.63 

 245.50 192.11 240.75 349.58 

 269.75 663.13 284.0 5111.2 

 284.0 3552.9 298.25 362.46 

 298.25 281.36 

1.0E-12

1.0E-10

1.0E-08

1.0E-06

1.0E-04

1.0E-02

0 20 40 60 80 100 120 140 160 180 200

SAMS 121f06 (on-orbit)

MAMS-HiRAP (on-orbit)

Mix/Melt (on ground)

Sedimentation (on ground)

 Frequency (Hz) 

Figure 4: PSD of EXPPCS On-orbit versus Ground Test Data For Two Different Sensors 

X-Axis PSD (g2/Hz)
NASA/TM2002-211813 15

1.0E-12

1.0E-10

1.0E-08

1.0E-06

1.0E-04

1.0E-02

0 20 40 60 80 100 120 140 160 180 200

SAMS 121f06 (on-orbit)

MAMS-HiRAP (on-orbit)

 Frequency (Hz) 

Figure 5: PSD of EXPPCS On-orbit versus Ground Test Data For Two Different Sensors 

1.0E-12

1.0E-10

1.0E-08

1.0E-06

1.0E-04

1.0E-02

0 20 40 60 80 100 120 140 160 180 200

SAMS 121f06 (on-orbit)

MAMS-HiRAP (on-orbit)

Mix/Melt (on ground)

Sedimentation (on ground)

 Frequency (Hz) 

Figure 6: PSD of EXPPCS On-orbit versus Ground Test Data For Two Different Sensors 

Y-Axis PSD (g2/Hz) Z-Axis PSD (g2/Hz) 
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21

Monitoring the Microgravity Environment Quality On-Board the International

Space Station Using Soft Computing Techniques

Part II: Preliminary System Performance Results

Kenol Jules, Paul P. Lin, and Daniel S. Weiss

Learning vector quantization; Kohonen's self-organizing feature map

Unclassified -Unlimited

Subject Categories: 19 and 35 Distribution: Nonstandard

Prepared for the 52nd International Astronautical Congress cosponsored by the International Astronautical Federation,

French Academy of Sciences, Air & Space National Academy, Floral World Academy, and the Art & Literature Academy,

Toulouse, France, October 1–5, 2001. Kenol Jules, NASA Glenn Research Center; Paul P. Lin, Cleveland State University,

Cleveland, Ohio; and Daniel S. Weiss, Harvard University, Cambridge, Massachusetts. Responsible person, Kenol Jules,

organization code 6727, 216–977–7016.

This paper presents the preliminary performance results of the artificial intelligence monitoring system in full operational mode using near real time

acceleration data downlinked from the International Space Station. Preliminary microgravity environment characterization analysis result for the

International Space Station (Increment-2), using the monitoring system is presented. Also, comparison between the system predicted performance based

on ground test data for the US laboratory “Destiny” module and actual on-orbit performance, using measured acceleration data from the U.S. laboratory

module of the International Space Station is presented. Finally, preliminary on-orbit disturbance magnitude levels are presented for the Experiment of

Physics of Colloids in Space, which are compared with on ground test data. The ground test data for the Experiment of Physics of Colloids in Space were

acquired from the Microgravity Emission Laboratory, located at the NASA Glenn Research Center, Cleveland, Ohio. The artificial intelligence was

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 of 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

dynamic graphical display, implemented in Java, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor,

crew exercise, structural modes, etc., and decide whether or not to run their experiments, whenever that is an option, based on the acceleration magnitude

and frequency sensitivity associated with that experiment. This monitoring system detects primarily the vibratory disturbance sources. The system has

built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen’s SelfOrganizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.