Intelligent Instrumentation: Principles and Applications

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ISBN: 9781420089530 | 1420089536
Cover: Hardcover
Copyright: 11/17/2010

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A reflection of the evolution of topics now covered in instrumentation or sensor engineering courses, this book employs principles-based explanations to delineate the technology. It uses commercial devices or technology to support the theory. The author provides solid answers to the questions: What makes a sensor intelligent? What are the various ways to classify them? How can a classical instrumentation system be provided with the intelligent features? and What are the recent technologies of II/SS? The book includes illustrations, photographs, numerical problems and case studies wherever needed to explain the theory.

Preface	p. xiii
Acknowledgments	p. xix
Author	p. xxi
Background of Instrumentation	p. 1
Introduction	p. 1
Process	p. 2
Process Parameters	p. 3
Classical Sensors and Transducers	p. 3
Classification	p. 4
Self-Generating Transducers	p. 5
Variable Parameter Transducers	p. 6
Resistance or Conductance Variation	p. 6
Capacitance Variation	p. 6
Magnetic Properties Variation	p. 7
Pulse or Frequency-Generating Type	p. 7
Radioactive Transducer	p. 8
Penetrating Power	p. 8
Half-Life	p. 9
Half-Distance	p. 9
Semiconductor Sensors	p. 10
Semiconductor Thermal Sensors	p. 10
Semiconductor Pressure Sensors	p. 12
Semiconductor Magnetic Sensors	p. 12
Hall-Effect Sensors	p. 12
Photodiodes and Phototransistors	p. 14
Array-Based Sensors	p. 16
Preprocessing	p. 16
Feature Extraction	p. 17
Classification and Decision Making	p. 17
Biosensors	p. 17
Actuating Devices	p. 17
Electrical Solenoids	p. 18
Electrical Motors	p. 18
References	p. 18
Further Readings	p. 19
Sensor Performance Characteristics	p. 21
Introduction	p. 21
Static Characteristics	p. 22
Accuracy and Precision	p. 22
Error, Correction, and Uncertainty	p. 25
Repeatability, Reproducibility, and Hysteresis	p. 27
Sensitivity, Offset, and Dead Band	p. 28
Resolution and Linearity	p. 31
Statistical Characteristics	p. 32
Error Modeling	p. 35
Dynamic Characteristics	p. 39
Dynamic Error and Dynamic Sensitivity	p. 39
Input-Output Impedances	p. 45
Reference	p. 51
Signals and System Dynamics	p. 53
Introduction	p. 53
Signal Representation	p. 53
Randomness of a Signal	p. 53
Analog, Binary, and Ternary Signals	p. 54
Test Signals	p. 56
Fourier, Laplace, and Z-Transform	p. 58
Spectral Density and Correlation Function	p. 62
Modifying and Modulating Inputs	p. 72
Compensation Techniques	p. 75
Interference Insensitive Design	p. 75
Negative Feedback	p. 76
Opposing Input Generation	p. 77
System Dynamics	p. 83
Zero-Order System	p. 83
First-Order System	p. 84
Electronic Noise Cancellation	p. 85
References	p. 86
Intelligent Sensors	p. 87
Introduction	p. 87
Classification	p. 87
Smart Sensors	p. 88
Monolithic Integrated Smart Sensors	p. 89
Hybrid Integrated Smart Sensors	p. 90
Cogent Sensors	p. 91
Soft or Virtual Sensors	p. 92
Soft Sensor Secondary Variable Selection	p. 94
Rough Set Theory	p. 94
Model Structures	p. 103
ARMAX Model	p. 103
Artificial Neural Network Models	p. 105
Self-Adaptive Sensors	p. 115
Algorithmic Adaptation with Nonredundant Time of Measurement	p. 117
Nonredundant Reference Frequency Measurement-Based Adaptation	p. 118
Frequency to Digital Conversion Using Microcontroller	p. 119
Adaptation to Linearity	p. 124
Self-Validating Sensors	p. 128
Functional Validation	p. 129
Numerical Method	p. 129
Artificial Intelligence for Sensor Validation	p. 140
Model-Based Approach	p. 141
Neural Network-Based Approach	p. 149
Applications of Self-Validating Sensors	p. 154
SEVA Model	p. 155
VLSI Sensors	p. 159
Analog Numerical Computation	p. 159
CORDIC Computation	p. 160
Adaptive Filtering	p. 165
LMS Algorithm	p. 166
Bit Stream Multiplication	p. 168
Image Processing	p. 170
Image Wavelets Transform	p. 171
Neural Network	p. 174
Analog VLSI-Based ANN	p. 175
Mathematical Operations	p. 180
Temperature Compensating Intelligent Sensors	p. 183
Effects of Temperature on Sensors	p. 184
Temperature Compensation Techniques	p. 188
Circuit Compensation	p. 189
Examples of Temperature Compensation in Sensor	p. 218
Temperature Compensation of Thermal Flow Sensors	p. 218
Temperature Compensation of Hot Film Crosswind Sensor	p. 224
Integrated Hardware Compensation of Pressure Sensor	p. 230
Integrated Compensation for Pressure Sensor	p. 236
Microcontroller-Based Compensation	p. 238
Frequency Control	p. 244
Sensitivity Slope Control	p. 254
Indirect Sensing	p. 263
Least Square Parameter Estimation	p. 264
Spectral Analysis-Based Parameter Estimation	p. 270
Fuzzy Logic Based	p. 275
References	p. 283
Linearization, Calibration, and Compensation	p. 287
Introduction	p. 287
Analog Linearization of Positive Coefficient Resistive Sensors	p. 288
Linearization by Shunt Resistance	p. 289
Positive Feedback OPAMP Circuit (Current Source)	p. 290
Positive Feedback OPAMP Circuit (Voltage Source)	p. 296
Linearization Using Feedback Amplifier	p. 303
Linearization of Negative Coefficient Resistive Sensors	p. 308
Higher-Order Linearization Using MOS	p. 315
Quadratic Linearization Using Divider Circuit	p. 316
Third-Order Linearization Circuit	p. 322
Nonlinear ADC-and Amplifier-Based Linearization	p. 325
Nonlinear Counting-Type ADC	p. 326
Nonlinear Successive Approximation ADC	p. 330
Pulse Width Modulation Nonlinear ADC	p. 332
Nonlinear Resistor-Based Amplifier	p. 338
Logarithmic Amplifier and FET-Based Linearization	p. 339
Interpolation	p. 347
Linear Interpolation	p. 347
Gradient Acceleration Factor	p. 349
Gradient Deceleration Factor	p. 354
Hardware Implementation of Linear Interpolation	p. 355
Progressive Polynomial Interpolation	p. 358
Cressman Interpolation	p. 359
Piecewise Linearization	p. 363
Analog Processing	p. 366
Digital Processing	p. 373
Lookup Table-Based ADC	p. 373
Piecewise Linear ADC	p. 378
Microcontroller-Based Linearization	p. 391
Lookup Table Method	p. 392
Artificial Neural Network-Based Linearization	p. 396
Nonlinear Adaptive Filter-Based Linearization	p. 399
Sensor Calibration	p. 402
Conventional Calibration Circuits	p. 403
Resistor Adjustment-Based Analog Calibration	p. 403
Digitally Programmable Resistor	p. 410
Multiplying DAC Calibration	p. 415
Offset Calibration	p. 419
Pulse-Modulated Calibration	p. 421
ADC Calibration	p. 423
Sigma-Delta ADC Calibration	p. 426
STIM Calibration	p. 432
Offset Compensation	p. 432
Improved Manufacturing Process	p. 432
Counter Bias Inputs	p. 433
Chopper Amplifier	p. 433
Matched Sensor Methods	p. 434
Error and Drift Compensation	p. 435
Drift Simulation	p. 436
Compensation by Complementary Sensors and Filters	p. 436
Lead Wire Compensation	p. 437
Self-Heating Method	p. 437
Bidirectional Current Mode RTD	p. 440
References	p. 442
Sensors with Artificial Intelligence	p. 445
Introduction: Artificial Intelligence	p. 445
Reasoning	p. 445
Knowledge	p. 445
Planning	p. 446
Learning	p. 446
Sensors with Artificial Intelligence	p. 446
Multidimensional Intelligent Sensors	p. 446
Gas Classification and Recognition	p. 447
Localization and Spatial Distribution	p. 449
AI for Prognostic Instrumentation	p. 452
Identification of Failure Predictors	p. 453
Sensors for Measurement of Conditions	p. 453
Data Storage, Processing, and Analysis	p. 453
Fault and Degradation Models	p. 453
Estimation of Failure and Degradation	p. 454
Artificial Intelligence-Based Prognostics	p. 454
ANN-Based Intelligent Sensors	p. 457
Linearization and Calibration by ANN	p. 458
Compensation of Error by ANN	p. 460
Soft Sensing by ANN	p. 464
Fault Detection by ANN	p. 467
Fuzzy Logic-Based Intelligent Sensors	p. 481
References	p. 482
Intelligent Sensor Standards and Protocols	p. 483
Introduction	p. 483
IEEE 1451 Standard	p. 483
STIM	p. 484
TEDS	p. 485
TEDS Calibration	p. 487
NCAP	p. 491
Network Technologies	p. 493
LonTalk	p. 495
LonTalk Media Types and Addressing	p. 495
CEBUS Communication Protocol for Smart Home	p. 496
CEBus Channels	p. 496
CEBus Architecture	p. 497
J1850 Bus	p. 498
Signal Logic and Format	p. 498
MI Bus	p. 499
Plug-n-Play Smart Sensor Protocol	p. 500
References	p. 502
Questions	p. 505
	p. 505
	p. 506
	p. 507
	p. 507
	p. 508
	p. 510
	p. 511
Index	p. 513
Table of Contents provided by Ingram. All Rights Reserved.

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