Fault Analysis of Analog Electronic Systems: Algorithms Based on

193 Pages·2012·9.31 MB·English

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X '7gorithims Based on Fuzzy Sets. . TERIM "
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Jonghee Leej
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9. PSEyRsF tOReMmsIN G- EOnRgGiAnNeIZeArT~IOiNn gN-A ME AN!De TpA DDhRE SS 7 10. PARROEGAR &A MW OERLKE MUENNITT , NPURMOBJEERCST, TASK c
University of Pennsylvania
Philadelphia, PA 19104
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16. SUPPLEMENTARY NOTES
Ph.D. dissertation in Systems Engineering
supervised by Professor S. D. Bedrosian
19. KEY WORDS (Continu. on reverse olde It necessay ind Identify by block number)
Fault analysis; Analog electronic systems; Fuzzy sets; Fuzzy algorithms
Fault diagnosis; Automated test equipment (ATE)
20. ABSTRACT (Continue or, tovorse aide It necessary and identify by block number)
There are essentially three fundamental problems involved in
achieving effective automatic generation of fault isolation
tests for analog electronic systems: feature extraction,
fault classification and diagnosis.
For practical electronic circuits having component drifts
and measurament noise, how are we able to introduce fuzzy set
concepts and provide methods to achieve fault classification
-
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20.
and diagnosis?
Along with the feature extraction problem, given an
electrical network of known topology, what are the conditions
for testability?
To attack the long standing fault isolation problem in
analog electronic circuits, we have focused on two of the
major problems. One is the presence of uncertainties such
as indeterminacy, vagueness, randomness, and so on that
naturally arise during the solution procedure of analog
fault isolation. The other is the presence of topological rest-
rictions inherent in specific circuit configurations. -
Our main attention was focused on dealing with the fault
isolation problem involving various kinds of uncertainties such
as indeterminacy or vagueness. We show that such problems lend
themselves very well to and in fact can be solved by adoptinc
fuzzy set concepts. In particular this line of research has
produced a modified fuzzy set technique applicable to automatic
fault isolation. Topological aspects utilizing graph theory may
be used effectively to assist in preanalysis of faulty analog
electronic circuits. As a spin off of a consideration of these
problems, we developed some new theorems for element value
solvability. It should be made clear however that effective
fault isolation can be accomplished with or without this pre-
analysis to assist in resolving the more fundamental problem
incurred by uncertainty.
As a consequence, this research yields the following
specific results:
1. A base line automatic isolation system which can be
used to lea. with various kinds of uncertainties. A fuzzy auto-
mation model served as a point of departure for the base line
system. Various fuzzy relations are used to select and update
the parameters and structures of Lhn system.
2. Set of algorithms and new decision criteria which can
be implemented easily and used for effective fault isolation. A
fuzzy distance measure and a fuzzy entropy measure are used for
decision making in the fault isolation algorithms. The results
are shown to be generally more effective than existing techniques
3. Ample illustrative examples and simulation studies are
included to back up these new methods. Several examples such
as low pass filter, band pass filter, and communcation I/0 cir-
cuits are used to illustrate the simulation studies. The results
of simulation studies demonstrate the applicability of a fuzzy
set technique.
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ABSTRACT
FAULT ANALYSIS OF ANALOG ELECTRONIC SYSTEMS:
Algorithms based on Fuzzy Sets
Jonghee Lee
Samuel D. Berosian
There are ossentially three fundamental problems
involved in achieving effective automatic generation of
fault isolation tests for analog electronic systems
tFor
feature extraction, fault classification and diagnosis.
practical electronic circuits having component
drifts and measurement noise, how are we able to introduce
fuzzy set concepts and provide method- to achieve fault
classification and diagnosis?
Along with the feature extraction problem, given an
electrical network of known topology, what are the
conditions for tes 'ty?
1
To attack the long standing fault isolation problem in
analog electronic circuits, we have focused on two of the
major pxoblems. One is the presence of uncertlinties such
as indeterminacy, vagueness, randomness, and so on that
natuially arise during the solution procedure of analog
fault isolation. The other is the presencr- of topological
restrictions inherent ii.s pecific circuit- configurations.
Our main attention was focused on dealing with the
fault isolation problem involving various kinds of
* I
A m m m m .m m m . . . ..
uncertainties such as indeterminacy or vagueness. We show
that such problems lend themselves very well to and in fact
can be solved by adopting fuzzy set concepts. In particular
this line of research has produced a modified fuzzy set
technique applicable to automatic fault isolation.
Topological aspects utilizing graph theory may be used
effectively to assist in preanalysis of faulty analog
electronic circuits. As a spin off of a consideration of
these problems, we developed some new theorems for element
value solvability. It should be made clear however that
effective f.iult isolation can be accomplished with or
without this preanalysis to assist in resolving the more
fundamental problem incurred by uncertainty.
As a consequence, this research yields the following
specific results:
1. A base linc automatic isolation system which
can be used to deal with various kinds of
uncertainties. A fu'zzy automaton model served
as a point of departure for the base line system.
Various fuzzy relations are used to select and
update the parameters and structures of the system.
2. Set of algorithms and new decision criteria which
can be implemented easily and used for effective
I,fa ult isolation. A fuzzy distance measure and a
fuzzy entropy measure are used for d, "
_: t -,*" ZL )
S_ n Htr
-
in the fault isolation algorithms. The results
are shown to be generally more effective than
existing techniques.-
3. Ample illustrative examples and simuJation studies
are included to back up these new methods. Several
examples such as low pass filter, band pass filter,
and communication I/O circuits are used to
illustrate the simulation studies. The results of
simulation studies demonstrate the applicability of
V
a fuzzy set technique.
i .
5
K, i
i-V
FAULT ANALYSIS OF ANALOG ELECTRONIC SYSTLMS:
Algorithms based on Fuzzy Sets
Jong". e Lee
A DISSERTATION
in
SYSTEMS ENGINEERING
Presented to the Graduate Faculties of the University of
Pennsylvania in Partial Fulfillment of the Requirements for
the Degree of Doctor of Philosophy.
1980
$upervisor of Dissertation
12
Graduate Grouo Chairpersor 1
.'
I
2
COPYRIGHT
Jonghee Lee
I
I
I
1980
I
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S
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ILK
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ACKNOWLEDGEMENTS
I wish to express my sincere thanks to Dr. S. D.
Bedrosian, my dissertation supervisor (and along the way
English teacher) for his interest, encouragement and advice,
I am also deeply indebted to Dr. D. W. C. Shen for his warm
interest and encouragement over the whole spectrum of
graduate studies at the Moore School.
Without the support of the Office of Naval Research on
Contract N00014-75-C-076y, this research would not have
been done.
Mcst of all I want to thank my wife, Iljoo, for her
belief, hope, and love.
.1
iv
LIS'i OF FIGURES
Fig. 3.1 Learning Curves with Reliable Teacher 40
Fig. 3.2 Learning Curves with Unreliable Teacher 41
(Max-min Relations)
Fig. 3.3 Learning Curves with Unreliable Teacher 42
(Max-Product Relations)
Fig. 3.4 Learning Curves with Unreliable Teacher 43
(Linear-Product Relations)
Fig. 3.5 Block Diagram of a Learning Fault 47
Diagnostic Scheme
Fig. 3.6 Flow Diagram for a Learning Diagnostic 59
Scheme in Fault Testing
Fig. 3.7 Circuit Diagram of Active Low Pass Filter 68
Fig. 3.8 Amplifier Characteristic 69
Fig. 3.9 Bode Plot 71
Fig. 3.10 Learning Curves for Sets of Discriminant 72
Functions
Fig. 3.11 Learning Curves for Refined Sets of 73
Discriminant Functions
Fig. 3.12 Learning Curves for Sets of Discriminant 74
Functions with Resolution
Fig. 3.13 Learning Curves for Refined Sets of 74
Discriminant Function with Resolution
Fig. 3.14 Membership Functions vs. Number of 75
Learning Iterations using Distance Measure
(3% component drift, 3% measurement noise)
Fig. 3.15 :embership Functions vs. Number of 76
Learning Iterations using Distance Measure