A prototype Optical Plume Anomaly Detection (OPAD) system is now installed on the space shuttle main engine (SSME) Technology Test Bed (TTB) at MSFC. The OPAD system requirements dictate the need for fast, efficient data processing techniques. To address this need of the OPAD system, a study was conducted into how artificial neural networks could be used to assist in the analysis of plume spectral data

Whitaker, Kevin W.

English

NASA Technical Reports Server

1992
NASA/ASEE SUMMER FACULTY FELLOWSHIP PROGRAM
MARSHALL SPACE FLIGHT CENTER
THE UNIVERSITY OF ALABAMA
NEURAL NETWORK ARCHITECTURES TO ANALYZE OPAD DATA
Prepared By:
Academic Rank:
Institution and Department:
Kevin W. Whitaker, Ph.D.
Assistant Professor
The University of Alabama
Department of Aerospace Engineering
NASA/MSFC:
Laboratory:
Division:
Branch:
Information and Electronic Systems
Guidance, Control, and Optical Systems
Sensors
MSFC Colleagues: W. T. Powers
Anita E. Cooper
LVII
https://ntrs.nasa.gov/search.jsp?R=19930008147 2020-03-17T09:10:13+00:00Z

INTRODUCTION
Plume emission spectroscopy (PES) can be applied to rocket engine testing by treating the engine
plume as a precisely-controlled laboratory flame for chemical analysis. Test stand or remotely-mounted
telescopes can collect engine plume emissions and direct the light, via a grating spectrometer system, onto a
linear array of silicon photodetectors. In a quantitative manner, light from many wavelengths of interest can
be compared to identify elements, ratioed to recognize alloys, or monitored as a function of time to establish
trends and the onset of significant material erosion.
The space shuttle main engine (SSME) became the subject of plume emission spectroscopy in 1986
when researchers from NASA-Marshall Space Flight Center (MSFC), Arnold Engineering Development
Center (AEDC), and Rocketdyne went to the SSME test stands at the NASA-Stennis Space Center and at
Rocketdyne's Santa Susana Field Laboratory to optically observe the plume. Since then, plume spectral
acquisitions have recorded many nominal tests and the qualitative spectral features of the SSME plume are
now well established. Significant discoveries made with both wide-band and narrow-band PES systems led
MSFC to promote the Optical Plume Anomaly Detection (OPAD) program with a goal of instrumenting all
SSME test stands with customized spectrometer systems.
A prototype OPAD system is now installed on the SSME Technology Test Bed ('ITB) at MSFC. The
OPAD system instrumentation consists of a broad-band, optical multiple-channel analyzer (OMA) and a
narrow-band device called a polychrometer. The OMA is a high-resolution (1.5-2.0 Angstroms)
"super-spectrometer" covering the near-ultraviolet to near-infrared waveband (2800-7400 Angstroms),
providing two scans per second. The polychrometer consists of sixteen narrow-band radiometers: fourteen
monitoring discrete wavelengths of health and condition monitoring species and two dedicated to monitoring
background emissions. All 16 channels are capable of providing 500 samples per second. To date, the
prototype OPAD system has been used during 35 SSME firings on the TTB, collecting well over 200
megabytes of plume spectral data.
The plume spectral data analysis and database correlation efforts to determine how much of a specie
(or element) is present and where it came from require the handling and processing of a massive database.
The data analysis is an incredibly labor intensive task and not one to be performed by hand. In the case of
engine monitoring for flight operations, which is the ultimate goal of the OPAD technology program, PES
contributions to a condition monitoring system must be made in real-time or near real-time in addition to
processing a large database. These OPAD system requirements dictate the need for fast, efficient data
processing techniques.
To address this need of the {)PAD system, a study was conducted into how artificial neural networks
could be used to assist in the analysis of plume spectral data.
WHY NEURAL NETWORKS.*
The latest trend in artificial intelligence (AI) research is the resurrection of artificial neural networks
(often simply called neural networks). Inspired by the working of the human brain and nervous system,
neural networks have recently offered breakthroughs in vision and speech processing technology.
Additionally, the systems have contributed to the practice of operations research providing insight to
decision-making methodology. Neural network research was originally conducted in the 1960's but the
research became dormant for almost the next two decades, primarily due to the inability to train a multiple-
layered network. This was overcome in the late 1980's and neural network research has started to flourish
once again with many successes.
A fundamental difference between the philosophies underlying conventional artificial intelligence
techniques (such as expert systems or rule-based systems) and neural networks involves the emphasis on
logical versus perceptive abilities. For example, AI in the form of expert systems employs logic to drive its
LVII-1
intelligence using rules and their logical combinations. Neural networks, on the other hand, are based on the
ability to recognize patterns through experience, rather than the deduction or recollection of rules. In fact,
neural networks can also be referred to as adaptive pattern recognition devices but, because of their flexibility
and ability to generalize, their applicability typically extends well outside the established realm of pattern
recognition. Since neural networks excel at tasks requiring the identification of relationships among large
amounts of data, such as is the case with the OPAD data, they are a superior alternative to the conventional
expert system approach.
VfRAT TYPE OF NEURAL NETWORK?
All neural networks have some type of structure associated with them. In fact, much research has
been conducted into the orientation of neuron layers, neuron interconnections, and the interconnection
weights. Unfortunately, parameters such as the number of neurons, the interconnection schemes, and the
network learning-rate strategy elude a priori assignment and require something of a "black art" to determine
them. Thus most neural network research centers around finding these network parameters.
Many neural network architectures exist. However, only a few are suited for analyzing OPAl) data
and these are discussed below.
Traditional Neural Networks
A traditional neural network is based on the concept of the adaptive linear element first studied in
detail by Widrow and Angell I in the early 1960's. The network consists of simple, highly-interconnected
processing dements called neurons. Each input signal to a processing element is amplified or dampened by
a weighting factor associated with the path from the signal source to the processing element. The processing
element collects all the weighted inputs and sums them to form a total weighted input. This weighted input
is passed to an activation threshold function, typically the mathematical function called the sigmoid. If the
weighted input exceeds a certain threshold, the neuron fires sending a signal along its output path to another
processing dement.
Neural systems solve problems by adapting to the nature of the data they receive. This is done
primarily through supervised training. In supervised training, a network is told whether its answer (output)
to a given input is correct or, ff incorrect, what the magnitude of the error is. If the network's answer is not
correct, the network begins to adjust the weights along each and every signal path to reduce the error
between the predicted output and the actual output. The most popular supervised training method is called
backpropagation, which was initially described by Werbos 2 in 1974, and later by Rummelhart et al.3 in 1987.
In short, hack-propagation dictates how error at the output layer is fed backwards through the network,
adjusting the path-weights along the way. Through repeated applications of a set of training data and
subsequent backpropagation of error, the weights eventually converge to an optimal configuration which
allows the neural network structure to identify patterns in any new data presented to it. fit is tacitly assumed
that the new data lies within or near a domain spanned by the training data.)
Probabilistic Neural Networks
The structure of a probabilistic neural network (PNN) is similar to a traditional network built using a
baclcpropagation training algorithm. The primary difference is that the sigmoidal activation function is
replaced by a class of functions which includes, in particular, the exponential. Probability is then used to
select which function in the class is used to determine whether or not the neuron fires. Theoretically, this
allows data patterns to be quickly imbedded in the network structure. It has been demonstrated <5 that
training a PNN is several orders-of-magnitude faster than training a traditional back-propagation network of
comparable size.
LVII-2
Fuzzy Neural Networks
In practice there exists a close relationship between neural networks and fuzzy logic systems since
they both work with degrees of imprecision in a space that is not defined by sharp, deterministic boundaries.
Fuzzy and neural technologies can be fused into a unified methodology known as fuzzy neural networks in
which the conventional control parameters in the neuron and the connection weights are replaced by fuzzy
regions 6.
A fuzzy neuron has an architecture that approximates the classical McCulloch-Pitts neuron 7. The
differences between a traditional neuron and a fuzzy neuron are evident when examining their respective
control properties. In place of scalar weights, fuzzy neurons use fuzzy sets as strength mediators of input
signals. When a signal is receive by a fuzzy neuron, it is summed along with all the other active input
signals to generate a cumulative signal. This cumulative signal is than mapped to a fuzzy set region
containing possible activation levels. Using a defuzzification methodology, such as centroid or minimum
entropy, the fuzzy region is collapsed to a scalar value representing the expected value of the fuzzy region
under the signal conditions. A conventional fuzzy alpha-cut threshold is used by the neuron to decide
whether or not the cumulative signal strength lies above a minimal gain boundary. If it is, the output of the
fuzzy neuron is roughly proportional to the fuzzy compatibility state, mapping the input signal strength to
the topology of the fuzzy threshold space. If the cumulative signal strength is below the threshold, the output
is zero.
Entropy Networks
As has been repeatedly discussed, a multiple-layer artificial neural network structure is capable of
recognizing patterns, or stated more formally, implementing arbitrary input-output mappings. Similarly,
hierarchical classifier systems, more commonly known as decision trees, posses the capability to generate
arbitrarily complex decision boundaries in an n-dimensional space. Exploiting this similarity, it is possible
to restructure a given decision-tree as a multiple-layered neural network. This mapping of decision-trees
into multiple-layered neural network structures can be used for the systematic design of a class of layered
neural networks called entropy nets9 which have far fewer intercormectious than a traditional neural
network. This means that the number of weights contained in the network is minimized while still
maintaining the generalizability of the network.
WHAT SHOULD BE DONE.'?
The raw spectral data obtained by the OPAl) system is voluminous, approaching seven megabytes
per SSME test-firing. That is the primary difficulty to be taclded by any AI system which hopes to analyze
(or help analyze) the data. Much hidden information is contained in each set of data. Patterns which exist
may be functions of many variables such as time, engine age, or operating conditions. Also, there is an
interplay between species data meaning that spectral signals must be looked at in unison rather that
separately. This is a classic problem of complex pattern recognition for which neural networks are well
suited.
The task at hand is to identify what type of neural network architecture should be utilized to analyze
OPAD data. Unfortunately, even with all the neural network research being conducted there are no reliable
rules for selecting neural network architectures for a given problem. Network selection and design is still
very much an art rather than a science. To that end, specific recommendations are put forth as how to select,
design, and apply neural network technology to the OPAD system.
Recommendation #1
All of the network architectures discussed have advantages and disadvantages when considering
them for use with the OPAl) system. With no basis for eliminating any of the network types, what should be
LVII-3
done is evaluate each type with a systematic approach. An investigation should be undertaken to first
construct neural networks of the various architecture types discussed in this report and then train each one
on a simplified subset of OPAl3 (or OPAD-like) data to learn how network behavior and architecture
interact. Items to be looked at should be ease of training final size of the trained network, generalizabflity,
and accuracy and speed of predictions.
Recommendation #2
Once each of the network types has been studied using a simplified set of data, they should be
designed and trained using full sets of OPAD data. Again, each network will be evaluated on its ease of
training, size, and generalizability. It is anticipated that at least one of the network architectures will prove
to be superior but until they are all trained and exercised with real OPAD data it is unknown which one that
will be.
Recommendation #3
Conduct a parallel research effort into developing new network architectures. It is conceivable that
the neural network architectures discussed may not be entirely appropriate for the OPAD system. However,
by combining some of the architectures it may be possible to develop a neural network which is, m essence,
customized for OPAD data analysis. For example, it may be possible to combine fuzzy set theory with
entropy nets to produce a network with an optimum number of nodes and the entropy net aspect will allow
existing decision-tree structures from expert systems currently in use at Stennis Space Center to be converted
into a neural network.
.
.
.
.
5.
.
7.
.
.
REFERENCES
Widrow, B., and Angell, J. B., "Reliable, Trainable Networks for Computing and Control," Aerospace
Engineering, Vol. 21, pp. 78-123, 1962.
Werbos, P., "Beyond Regression: New tools for Prediction and Analysis in the Behavioral Sciences,"
Ph.D. Dissertation, Committee on Applied Mathematics, Harvard University, Nov. 1974.
Rummelhart, D. E., Hinton, G. E., and Williams, IL J., "Learning Internal Representations by Error
Propagation," Nature, Vol. 323, pp. 318-362, 1986.
Specht, D. F., "Probabilistic Neural Networks," NeuralNetworks, Vol. 3, pp. 109-118, 1990.
Specht, D. F., "Probabilistic Neural Networks and the Polynomial Adaline as Complementary
Techniques for Classification," IEEE Transactions on Neural Networks, "Col. 1, No. I, pp. 111-121,
1990.
Cox, E., "Integrating Fuzzy Logic into Neural Nets," AI Expert, pp. 43-47, June 1992.
McCulloch, W. W., and Pitts, W., "A Logical Calculus of the Ideas Imminent in Nervous Activity,"
Bulletin of Mathematical Biophysics, Vol. 5, pp. 115-133, 1943.
Rununelhart, D. E., "Leam/ng Internal Representations by Error Propagation," Parallel Distributed
Processing, VoL 1, pp. 318-362, 1986.
Sethi, I. K., "Entropy Nets: From Decision Trees to Neural Networks," Proceedings of the IEEE, Vol.
78, No. 10, pp. 1605-1613, 1990.
LVII-4


Neural network architectures to analyze OPAD data

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server