The SPARTA Embedded Expert System (SEES) is an intelligent health monitoring system that directs analysis by placing confidence factors on possible engine status and then recommends a course of action to an engineer or engine controller. The technique can prevent catastropic failures or costly rocket engine down time because of false alarms. Further, the SEES has potential as an on-board flight monitor for reusable rocket engine systems. The SEES methodology synergistically integrates vibration analysis, pattern recognition and communications theory techniques with an artificial intelligence technique - the Embedded Expert System (EES)

Homsley, T.

Jones, J.

Lewallen, P.

Pooley, J.

Teoh, W.

Thompson, W.

English

NASA Technical Reports Server

N88-16382
EMBEDDED EXPERT SYSTEM FOR
SPACE SHUTTLE MAIN ENGINE MAINTENANCE
BY:
J. Pooley, W. Thompson, T. Homsley, and W. Teoh, PhD
SPARTA, Inc.
4901 Corporate Drive
Huntsville, Alabama 35805
J. Jones and P. Lewallen
NASA Marshall Space Flight Center
Huntsville, Alabama
ABSTRACT
Space Shuttle Main Engine (SSME) maintenance, whether preventive, scheduled, or
unscheduled, is a major escalating cost item. Significant progress has been made in the NASA
and Air Force communities toward performance of the health monitoring function in
instrumentation, analysis techniques, and envelope (trends and rate of change) monitoring.
Current techniques require that domain experts be integrally involved in the analysis session and
make on-line decisions to direct analysis. The SPARTA Embedded Expert System (SEES) is an
intelligent health monitoring system that directs the analysis by placing confidence factors on
possible engine status, then recommends a course of action to an engineer or the engine
controller. This technique can prevent catastrophic failures or costly rocket engine down time
because of false alarms. Further, the SEES has potential as an on-board flight monitor for reusable
rocket engine systems. The SEES methodology synergistically integrates vibration analysis,
pattern recognition, and communications theory techniques with an artificial intelligence
technique - the Embedded Expert System (EES). This integration affords a robustness via the
analysis techniques with an ability to resolve conflicts by the expert system techniques.
INTRODUCTION
A critical element of the Space Shuttle Main Engine (SSME) program is the development of a
turbo-pump health monitoring system (HMS). A HMS that could predict incipient failures and
permit routine maintenance to be scheduled based on performance indicators would dramatically
reduce the need for refurbishment, improve equipment availability, and make maintenance more
cost-effective. The key functions of an effective HMS are shown in Figure 1.
• RECOGNIZE AND CATEGORIZE PERFORMANCE
(Baselinlng Of Performance Standards)
• RECOGNIZE AND CORRELATE INDICATORS OF IMPENDING
FAILURE
(Incipient Failure Prediction)
• RECOGNIZE AND CORRELATE INDICATORS OF NEED FOR
REMEDIAL ACTION
(Scheduling Of Routine Maintenance In A Cost-Effective
Manner)
Figure 1. HMS ESSENTIAL FUNCTIONS
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Significant progress has been made in the NASA community toward performance of the
HMS functions. There have been relevant advances in instrumentation [4,1], analysis techniques
[2,5], and in detection of anomalies and failures [3]. Each of these advances has demonstrated
individual attributes useful for an HMS to correlate failure modes with turbo-pump components at
risk. However, an integrated HMS that uses and updates the SSME data base is possible through
the use of emerging AI techniques. AI techniques, specifically a rule-based expert system, can
enhance the functions of an HMS. SPARTA has developed and adapted a set of algorithms to
produce an innovative application of Artificial Intelligence techniques. The keystone of this
application is an expert system that uses confidence levels to resolve conflicts among compound
data, and that heuristically trains on each data set to derive (or modify) classification rules. This
expert system has been named SEES, an acronym for SPARTA Embedded Expert System.
_EE_. ARCHITECTURE
The SEES architecture is shown in Figure 2. In SEES, conventional computation methods
are used to reduce the raw data to a manageable "derived" data set, and to extract pertinent
information (signatures) from the derived data set. This information is then used by the SEES to
derive rules, with the help of domain experts/knowledge engineers, to establish a knowledge
base. In future phases, SEES will use this set of rules to determine engine conditions during
SSME testing.
MAJOR COMPONENTS
As can be seen from the architecture in Figure 2, there are three major subsystems to the
SEES HMS: The SEES front end (SFE), the embedded expert system (EES), and the
supportfunction library (SFL). The SFE processes the raw data to screen obvious anomalies and
to derive the reduced data set, then generates from it an appropriate signature. The process of
data screening, reduction and signature generation is the unique and proprietary innovation of
SEES. The embedded expert system (EES) uses this signature and the reduced data, with the
help of the SFL and the rule set in its knowledge base, to infer the operating conditions at a given
instant, deduce the mean time to failure and recommend maintenance schedules. The SFL, as its
name implies, is a set of supporting functions for the rest of the HMS.
SEES FRONT END (SFE)
The SFE is comprised of signal analysis techniques that convert raw count accelerometer
data to Engineering Units and transform the data to the frequency domain using Fast Fourier
Transforms (FFT) to derive a power spectral density (PSD) for inputto a Data Conditioning Module.
The Data Conditioning Module processes the PSD signal to remove the extraneous components.
Finally, the conditioned PSD is evaluated as a candidate for signatures derived during this
processing (by the Pattern Marcher) or binned to be considered for establishment of another
signature.
SEES SUPPORT FUNCTION LIBRARY (SFL)
The SFL consists of the algorithms that transform reduced data into symbol structures for
use by the Development Engine and/or the Inference Engine to accomplish inference and control.
This transformation is accomplished by applying communications theory and image processing
methods to the SFE conditioned data.
SEES EMBEDDED EXPERT SYSTEM
The Embedded Expert System (EES) is an integral part of the HMS. The EES is a rule based
knowledge system that uses forward chaining strategy, and has the ability to recognize and
categorize performance, incipient failure and the need for remedial action. It consists of a
development engine, a knowledge base, an inference engine, and a user interface.
116
Th_ DeveloPment Engine
The development engine is a subsystem of EES which is intimately related to the knowledge
acquisition process; it allows a knowledge engineer to transcribe the knowledge gained from the
domain expert into a set of rules that make up the knowledge base. A basic characteristic of the
SEES problem in analyzing SSME vibration data is the volatility of signatures and the importance
of high rate vibration data. While some rules can be developed, the evaluation of data in real-time
leads to the requirement to merge information from multiple sources. This leads to the use of the
blackboard architecture for storing intermediate hypotheses, the use of a certainty factor merging
heuristic and rule-use counting as a "rule-critic".
The SEES Knowledae Base
Based on SPARTA's study of the training sets, we expect the knowledge base to be quite
large. Our investigation indicated that signatures may be extracted and meaningfully classified.
Thus, the rule set may be ordered in an appropriate manner (e.g., a rule tree) to reduce search
space. The nature of the data is such that one can seldom specify a diagnosis with absolute
certainty. Thus in SEES, certainty factors will be used to reflect uncertain information. These
certainty factors can be either computed algorithmically by the development engine based on
derived or existing knowledge, or estimated by domain experts or knowledge engineers.
The SEES Inference Enaine
The SEES HMS is basically data driven. Thus, a forward chaining strategy is appropriate.
The incoming data, although reduced by the SFE, is still quite complex, and entering the HMS at a
high rate. The EES inference engine must and will have the capability to invoke functions in the
SFL for further data reduction. Perhaps one of the more important tasks of the inference engine is
to determine when an unknown situation (i.e., not in the rule base) occurs. It should be able to
coordinate with the domain expert or knowledge engineer and pass the new information to the
development engine to create new rules, or store the information to accomplish the same at a later
time off-line when a domain expert is available. The inference engine must provide information to
the explainer to produce explanation on demand. The explainer is a subsystem of the user
interface and can provide explanations as to how a conclusion is reached. This can be
accomplished in a variety of ways; the one selected by SPARTA is to leave the time history of
SEES events in the blackboard for post mortem examination.
SEES User Interface
The user inter/ace is the component of an expert system that acts as an interface between
the expert system and the user who is not necessarily a domain expert. Thus, it should have the
capabilities to" (1) Solicit input from the user, (2) Provide output to the user - this output may be in
the form of questions, recommendations or conclusions, and (3) Provide explanations on
demand. One important aspect that can be implemented is a possible data link between the user
interface and the SSME controller. This would serve as a means to assume control of the engine in
unusual situations, such as when an imminent engine failure has been detected, and an immediate
engine shutdown becomes necessary to prevent catastrophic failure. This feature is, of course,
not needed for off-line testing.
IMPLEMENTATION
Preliminary investigation to date has been carried out using a VAX780 computer in
FORTRAN. It is anticipated that the final system will be implemented in FORTRAN or C to run on a
MASCOMP computer. This computer is chosen because of the outstanding data acquisition
capabilities which is a critical aspect of SEES. Equally important is the fact that a variety of
languages and utilities are available commercially for this micro-supercomputer. A decision has to
be made as to the language used to develop the embedded expert system (EES). One can
choose the more traditional approach of using LISP or PROLOG (both of which are available to the
MASCOMP, and both can interface with the rest of the system if it is written in C. We have decided
117
touseRULEMASTERbyRadianCorporation.Thisisanexpertsystemshellthatwouldallow us to
develop EES rapidly, and can be integrated to the rest of SEES easily.
PRELIMINARY RESULTS
The vibration time series is analyzed in a discrete data format. The data is first transformed
into a power spectral density (PSD). Each discrete PSD is the power average over a small time
interval at a frequency with a certain bandwidth. The power level of a frequency line is then
temporalized. Figure 3 shows the amplitude time history of one important frequency band. The
accompanying SSME power profile shows the shift from 100% to 104% power level. The
amplitude of the time history shows a marked decrease at that time. Other bands show an increase
at ramp up to 104%. The characterized signatures consist of a covariance matrix, C, which
measures coupling between components of the sample vectors and the mean sample vector, M.
A signature is a measure of the turbo-pump's performance profile at a given load condition. When
a turbo-pump is operated at a load condition for an extended period, its performance may degrade
from nominal to anomalous. This degradation is measured by the HMS and characterized into a
class ensemble of signature, at a load condition. Two signatures characterized from the SSME test
are presented in Figure 4. The spectral components of these signatures are very complicated;
therefore, AI techniques must be used to classify data.
CONCLUSIONS
Preliminary analysis has shown that the SEES development engine successfully extracts
signatures from SSME test data that can be formulated into rules for the SEES knowledge base.
B.EEF,3EU_C,.E 
[1] Barkhoundarian, S. and A.T. Zachary, "Condition Monitoring For Space-Based Reusable
Rocket Engines", ASME Winter Annual Meeting, New Orleans, LA, December 1984.
[21 Coffin, T. and J.Y. Jong, "Signal Detection Techniques for Diagnostic Monitoring Of Space
Shuttle Main Engine Turbomachinery", NASA Technical Report 66938-01, February
1986.
[3] Evatt, J.C. and W.W. Palmquist, "Failure Control Techniques for the SSME", NAS8-36305,
Phase I Final Report, Rockwell International.
[4] Hampson, M.E., J.J. Collins, M.R. Randell, and S. Barkhoundarian, "SSME Bearing Health
Monitoring Using a Fiberoptic Deflectomer', NASA Conference on Advanced Earth-to-
Orbit Propulsion Technology, Huntsville, Alabama, May 1986.
[5] Jong, J.Y. and Thomas Coffin,
Coherence Method", NASA
Technology, May 1986.
"Diagnostic Assessment of Turbomachinery by Hyper-
Conference on Advanced Earth-to-Orbit Propulsion
118
E ]CONTROLLER
DATA ACQUISITION
AND TRANSFORMATION
CALIBRATION
FFT/PSD
CHANNELIZATION
FEATURE EXTRACTION
I OATACOND,T,ON,NG]
SIGNATURE
CHARACTERIZATION
ORIGINA.L PA.G_ l_
C,r POOR QUA.UT3fl
EES
|
I DATA I
I BASl I
'--r--
|
I RUN
_DEVELOPMENT w
USER
USER )INTERFACE
tNFERENCEENGINE
T
KNOWLEDGEBASE I
Y
DEVELOPMENTENGINE
DOMAIN EXPERTS
FIGURE 2. SEES - HMS ARCHITECTURE
SFL
SUPPORTING FUNCTIONS
LIBRARY
• CHARACTERIZATION
BY MOMENTS
• CEPSTRUM
• HYPER
COHERENCE
• PATTERN
MATCHER
• SIGNATURE
SEPARABILITY
• OISCRIMINATO R
• DIVERGENCE
• BASELINE
PERFORMANCE
• FEATURE
SELECTION
0387-044/0_
N
(J
t _ t 2
12 f T
10
8
4
2 ll
i
0 2_5
t3 t 4
' 5_0 75
t s t e t 7 t $
i i F J
1
100 125 IS0 175 200 225 250
TIME (=ec)
TIMES OF
SIGNATURE
INITIATION
I @ 16.2 t 1
2 @ 30.2 t 2
3 @ 9$.2 t 3
4 @ 100.2 t 4
S _ 312.4 t S
E @ 126.6 t E
7 0 20S.O t7
0 @ 224.0 t 8
112
110
108
0 106
a.
" 104
O 102
_00
i i ii I i I i
215 SlO
I J i ' '
75 100 125 150 175 200 225 250
TIME (see)
FIGURE 3. TEMPORALIZED DATA OF CHANNELS FROM SSME TEST #A2-356-5042
10.00 t
1.00
0.10 [_
0.01
0
SIGNATURE 1
YNAMICALLY EXTRACTEO AT 16.2 SECONDS FROM
$SME TEST SA2-3S&-SO42 AT POWER LEVEL 104)%
L
128 256 384 512 640 768 090 1024
FREQUENCY LINES
_o _2eo 192o2._o 32oo=.o ,._s_'2o o
FREQUENCY INi)
FIGURE 4. TYPICAL SSME SIGNATURES
10"00 l
1.00
i 0,10
O,D't
SIGNATURE 2
DYNAMICALLY EXTRACTED AT 30.2 SECONDS FROM
SSME TEST A2-3Se-SO42 AT POWER LEVEL 100%
128 256 384 512 640 768 890 I024
FRXQUX_CY LINI[X
FREQUENCY IHI)
0387-013/14
119


Embedded expert system for space shuttle main engine maintenance

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