Multisensor Instrumentation 6? Design: Defined Accuracy Computer-Integrated Measurement Systems

218 Pages·2002·3.35 MB·English

by  Patrick H. Garrett(auth.)

MULTISENSOR
INSTRUMENTATION
(cid:1)(cid:1)
6 DESIGN
MULTISENSOR
INSTRUMENTATION
(cid:1)(cid:1)
6 DESIGN
Defined Accuracy
Computer-Integrated
Measurement Systems
PATRICK H. GARRETT
A Wiley-Interscience Publication
JOHN WILEY & SONS, INC.
This book is printed on acid-free paper. (cid:2)
Copyright © 2002 by John Wiley & Sons, Inc., New York. All rights reserved.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data:
Garrett, Patrick H.
Multisensor instrumentation 6[sigma] design / Patrick H. Garrett
p. cm.
Title has numeral 6 followed by Greek sigma.
“A Wiley-Interscience publication.”
ISBN 0-471-20506-0 (cloth)
1. Electrooptical devices—Testing—Congresses. 2. Automatic checkout
equipment—Congresses. I. Title.
TA1750 .G37 2001
670.42'7—dc21 2001046730
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
CONTENTS
Preface ix
1 Process, Quantum, and Analytical Sensors 1
1-0 Introduction 1
1-1 Instrumentation Error Representation 1
1-2 Temperature Sensors 4
1-3 Mechanical Sensors 7
1-4 Quantum Sensors 12
1-5 Analytical Sensors 17
Bibliography 23
2 Instrumentation Amplifiers and Parameter Errors 25
2-0 Introduction 25
2-1 Device Temperature Characteristics 25
2-2 Differential Amplifiers 26
2-3 Operational Amplifiers 31
2-4 Instrumentation Amplifiers 34
2-5 Amplifier Parameter Error Evaluation 42
Bibliography 45
3 Active Filter Design with Nominal Error 47
3-0 Introduction 47
3-1 Lowpass Instrumentation Filters 47
3-2 Active Filter Networks 52
3-3 Filter Error Analysis 60
3-4 Bandpass Instrumentation Filters 65
Bibliography 74
v
vi CONTENTS
4 Linear Signal Conditioning to Six-Sigma Confidence 75
4-0 Introduction 75
4-1 Signal Conditioning Input Considerations 75
4-2 Signal Quality Evaluation and Improvement 78
4-3 DC, Sinusoidal, and Harmonic Signal Conditioning 84
4-4 Redundant Signal Conditioning and Diagnostics 89
Bibliography 93
5 Data Conversion Devices and Errors 95
5-0 Introduction 95
5-1 Analog Multiplexers 95
5-2 Sample-Holds 97
5-3 Digital-to-Analog Converters 100
5-4 Analog-to-Digital Converters 106
Bibliography 119
6 Sampling and Reconstruction with Intersample Error 121
6-0 Introduction 121
6-1 Sampled Data Theory 121
6-2 Aliasing of Signal and Noise 126
6-3 Step-Interpolated Data Intersample Error 130
6-4 Output Signal Interpolation, Oversampling, and Digital Conditioning 136
Bibliography 144
7 Measurement and Control Instrumentation Error Analysis 147
7-0 Introduction 147
7-1 Low-Data-Rate Digital Control Instrumentation 147
7-2 High-Data-Rate Video Acquisition 155
7-3 Computer-Integrated Instrumentation Analysis Suite 157
Bibliography 168
8 Multisensor Architectures and Error Propagation 169
8-0 Introduction 169
8-1 Multisensor Fusion, Integration, and Error 169
8-2 Sequential Multisensor Architecture 172
8-3 Homogeneous Multisensor Architecture 174
8-4 Heterogeneous Multisensor Architecture 179
Bibliography 186
9 Instrumentation System Integration and Interfaces 187
9-0 Introduction 187
9-1 System Integration and Interface Buses 187
CONTENTS vii
9-2 Instrument Serial Bus Interfaces 191
9-3 Microwave Microscopy Virtual Instrument 197
9-4 Analytical Instrumentation in Advanced Control 201
Bibliography 207
Index 209
PREFACE
Over the past decade, technical innovation and evolution in computer-centered
measurement systems have led to significant performance advances and economies
of scale from manufacturing processes to biotechnology laboratories that share
multisensor information system common denominators. This book provides a de-
finitive instrumentation circuit and system reference, supported by 46 tables of en-
gineering data, that demonstrates a composite, error-modeled design methodology
for implementing defined accuracy, computer integrated measurement systems. The
comprehensive accountability presented is consistent with six-sigma quality met-
rics, and features a user-interactive analysis spreadsheet for the performance opti-
mization of instrumentation designs through total error minimization.
An expanding reliance on six-sigma methods for process optimization (define-
measure-analyze-improve-control) emphasizes the significance of data measure-
ment accountability, especially as it affects integrated information structures and au-
tomatic control systems. Accordingly, highlights of this book include end-to-end
instrumentation performance modeling in terms of comprehensive mean and RSS er-
rors, signal conditioning capabilities extending to sensor noise thresholds, sampled-
data design guided by definitive intersample error metrics, and evaluation of algo-
rithmic error propagation in complex multisensor architectures.
The first chapter presents a compendium of sensors with a signal model hierarchy
of ascending complexity from apparatus, to in situ, to analytical measurements. The
next three chapters cover linear signal conditioning devices and circuits with five cat-
egories of instrumentation amplifier sensor interfaces for the upgrading of microvolt
signals immersed in volts of random and coherent interference. In the following two
chapters, data conversion devices and their performance are analyzed, including sev-
en application-specific A/D converter types. Digital data conversion system design
employs intersample error to evaluate sampled data influences ranging from noise
aliasing, to oversampling, to the effectiveness of various output signal interpolation
functions, including closed-loop bandwidth in digital control systems.
The final three chapters present diverse instrumentation system examples illus-
trating this error-modeled design approach from process controllers, to video digiti-
ix
x PREFACE
zation, to vibration analyzers. Multisensor error propagation is described by de-
tailed examples of sequential, homogeneous, and heterogeneous architectures
shown, respectively, by turbine engine airflow, electric machine health monitoring,
and materials manufacturing process instrumentation. Instrumentation system inte-
gration is then examined in a progression from discrete instruments, to remote I/O,
to virtual instruments, to analytical instruments exemplified, respectively, by auto-
matic test equipment, satellite meteorology instrumentation, programmable mi-
crowave microscopy, and analytical instruments for advanced control. This taxono-
my includes interfaces from FireWire to Gigabit Ethernet. In fact, analytical
instrumentation in contemporary process systems typically performs at a higher
level of abstraction than associated control algorithms.
Many of the developments presented have not appeared in other books or papers.
Notable are derivations of device errors such as filter mean error and signal error
quantitation resulting from linear input signal conditioning operations and sampled
output signal interpolator effectiveness. The author accepts responsibility for the
ideas presented and any shortcomings, and hopes that they may stimulate further
study and contribution to these topics.
PATRICKH. GARRETT
Multisensor Instrumentation 6(cid:1)Design. By Patrick H. Garrett
Copyright © 2002 by John Wiley & Sons, Inc.
ISBNs: 0-471-20506-0 (Print); 0-471-22155-4 (Electronic)
1
PROCESS, QUANTUM,AND
ANALYTICAL SENSORS
1-0 INTRODUCTION
Automatic test systems, manufacturing process control, analytical instrumentation,
and aerospace electronic systems all would have diminished capabilities without
the availability of contemporary computer integrated data systems with multisensor
information structures. This text develops supporting quantitative error models that
enable a unified performance evaluation for the design and analysis of linear and
digital instrumentation systems with the goal of compatibility of integration with
other enterprise quality representations.
This chapter specifically describes the front-end electrical sensor devices for a
broad range of applications from industrial processes to scientific measurements.
Examples include environmental sensors for temperature, pressure, level, and flow;
in situ sensors for measurements beyond apparatus boundaries, including spectrom-
eters for chemical analysis; and ex situ analytical sensors for manufactured material
and biomedical assays such as microwave microscopy. Hyperspectral sensing of
both spatial and spectral data is also introduced for improved understanding
through feature characterization. It is notable that owing to advancements in higher
attribution sensors, they are increasingly being substituted for process models in
many applications.
1-1 INSTRUMENTATION ERROR REPRESENTATION
In this text, error models are derived employing electronic device, circuit, and sys-
tem parameter values that are combined into a unified end-to-end performance rep-
resentation for computer-based measurement and control instrumentation. This
methodology enables system integration beneficial to contemporary technologies
ranging from micromachines to distributed processes. Since the baseline perfor-
mance of machines and processes can be described by their internal errors, it is ax-
iomatic that their performance may also be optimized through design in pursuit of
1
2 PROCESS, QUANTUM, AND ANALYTICAL SENSORS
error minimization. Instrumentation system errors are interpreted graphically in
Figure 1-1. Total error is shown as the composite of barred mean error contributions
plus the root-sum-square (RSS) of systematic and random uncertainties; the true
value is ultimately traceable to a reference calibration standard harbored by NIST.
Although total error may instantaneously be greater or less than mean error from
the additivity of RSS uncertainty error, throughout this text total error is expressed
as the sum of mean and RSS errors in providing accountability of system behavior.
Total error is analytically expressed by equation (1-1) as 0–100% of full scale
(%FS), where the RSS sum of variances represents a one-sigma confidence interval.
Consequently, total error may be expressed over any confidence interval by adding
one mean error value and a summation of RSS error values corresponding to the stan-
dard deviation integer. Confidence to six sigma is therefore defined by mean error
plus six times the RSS error value. Mean error frequently arises in instrumentation
systems from transfer function nonlinearities that, unlike RSS uncertainty error,
which may be reduced by averaging identical systems as shown in Chapter 4, Section
4-4, instead increases with the addition of each mean error contribution, necessitat-
ing remedy through minimal inclusion. Accuracy is defined as the complement of er-
ror (100%FS – (cid:2)%FS), where 1%FS error corresponds to 99%FS accuracy.
A six-sigma framework is accordingly offered in terms of models defining mul-
tisensor instrumentation errors to provide a generic design and analysis methodolo-
gy compatible with corollary enterprise-quality representations. Quantitative instru-
mentation system performance expressed in terms of modeled errors assumes that
external calibration is maintained to known standards, as shown in Figure 1-21, ver-
ifying zero and full-scale values for external instrumentation registration. Calibra-
tion is essential and can be performed manually or by automated methods, but it
cannot characterize instrumentation device, circuit, and system variabilities that
these error budgets ably describe, including expression to 6(cid:1)confidence.
(cid:2) = (cid:3) (cid:1)(cid:2)(cid:1)(cid:1)(cid:1)(cid:1)%FS + [(cid:3) (cid:2)2 + (cid:3) (cid:2)2 ]1/2%FS1(cid:1) (1-1)
total mean systematic random
FIGURE1-1. Instrumentation error interpretation.