Auxiliary Signal Design for Failure Detection

Many industries, such as transportation and manufacturing, use control systems to insure that parameters such as temperature or altitude behave in a desirable way over time. For example, pilots need assurance that the plane they are flying will maintain a particular heading. An integral part of cont...

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Bibliographic Details
Main Authors:	Campbell, Stephen L, Nikoukhah, Ramine
Format:	eBook Book
Language:	English
Published:	Princeton, NJ Princeton University Press 2004
Edition:	1
Series:	Princeton Series in Applied Mathematics
Subjects:	A priori estimate Abuse of notation Additive white Gaussian noise Algorithm Applied Approximation Asymptotic analysis Bisection method Boundary value problem Calculation Catastrophic failure Computation Condition number Control theory Control variable Decision theory Derivative Detection Deterministic system Diagram (category theory) Differential equation Discretization Dynamic programming Engineering Engineering design process Equation Error message Estimation Estimation theory Fault location (Engineering) Gain scheduling Initial condition Integrator Laplace transform Least squares Likelihood function Likelihood-ratio test Limit point Linearization Mathematical optimization Mathematical problem MATHEMATICS MATHEMATICS / Applied Maxima and minima Measurement Method of lines Nonlinear control Nonlinear programming Observational error Open problem Optimal control Optimization problem Parameter Partial differential equation Piecewise Pointwise Prediction Probabilities & applied mathematics Probability Process Design, Control & Automation Remedial action Riccati equation Sampling (signal processing) Scilab Sensors and Instrumentation for Control & Measurement Shift operator Signal processing Signal Processing and Analysis Special case Stochastic Stochastic calculus Stochastic interpretation Stochastic Modeling Stochastic process System failures (Engineering) Time complexity Trade-off Transfer function Transient response Uncertainty Engineering design process Limit point Stochastic Stochastic process Partial differential equation Stochastic interpretation Calculation Open problem Nonlinear programming Laplace transform Additive white Gaussian noise Nonlinear control Linearization Likelihood function Approximation Estimation Trade-off Prediction Mathematical problem Gain scheduling Algorithm Control variable Scilab Boundary value problem Computation Decision theory Least squares Method of lines Derivative Piecewise Special case Detection Shift operator Mathematical optimization Time complexity Measurement Uncertainty Sampling (signal processing) Catastrophic failure Differential equation Diagram (category theory) Maxima and minima Engineering Discretization Pointwise Stochastic Modeling Integrator Likelihood-ratio test Remedial action Dynamic programming Asymptotic analysis Transfer function Transient response Observational error Initial condition Estimation theory Abuse of notation Probability Error message Equation A priori estimate Stochastic calculus Optimal control Condition number Bisection method Parameter Deterministic system Control theory Riccati equation Optimization problem
ISBN:	1400880041, 9781400880041, 9780691099873, 0691099871
Online Access:	Get full text
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Table of Contents:

Auxiliary signal design for failure detection -- Contents -- Preface -- Chapter One: Introduction -- Chapter Two: Failure Detection -- Chapter Three: Multimodel Formulation -- Chapter Four: Direct Optimization Formulations -- Chapter Five: Remaining Problems and Extensions -- Chapter Six: Scilab Programs -- Appendix A: List of Symbols -- Bibliography -- Index.
Front Matter Table of Contents Preface Chapter One: Introduction Chapter Two: Failure Detection Chapter Three: Multimodel Formulation Chapter Four: Direct Optimization Formulations Chapter Five: Remaining Problems and Extensions Chapter Six: Scilab Programs Appendix A. Bibliography Index
Title Page Preface Table of Contents 1. Introduction 2. Failure Detection 3. Multimodel Formulation 4. Direct Optimization Formulations 5. Remaining Problems and Extensions 6. Scilab Programs Appendix A: List of Symbols Bibliography Index
Cover -- Title -- Copyright -- Contents -- Preface -- Chapter 1. Introduction -- 1.1 The Basic Question -- 1.2 Failure Detection -- 1.3 Failure Identification -- 1.4 Active Approach versus Passive Approach -- 1.5 Outline of the Book -- Chapter 2. Failure Detection -- 2.1 Introduction -- 2.2 Static Case -- 2.3 Continuous-Time Systems -- 2.4 Discrete-Time Systems -- 2.5 Real-Time Implementation Issues -- 2.6 Useful Results -- Chapter 3. Multimodel Formulation -- 3.1 Introduction -- 3.2 Static Case -- 3.3 Continuous-Time Case -- 3.4 Case of On-line Measured Input -- 3.5 More GeneralCost Functions -- 3.6 Discrete-Time Case -- 3.7 Suspension Example -- 3.8 Asymptotic Behavior -- 3.9 Useful Results -- Chapter 4. Direct Optimization Formulations -- 4.1 Introduction -- 4.2 Optimization Formulation for Two Models -- 4.3 General m-Model Case -- 4.4 Early Detection -- 4.5 Other Extensions -- 4.6 Systems with Delays -- 4.7 Setting Error Bounds -- 4.8 Model Uncertainty -- Chapter 5. Remaining Problems and Extensions -- 5.1 Direct Extensions -- 5.2 Hybrid and Sampled Data Systems -- 5.3 Relation to Stochastic Modeling -- Chapter 6. Scilab Programs -- 6.1 Introduction -- 6.2 Riccati-based Solution -- 6.3 The Block Diagonalization Approach -- 6.4 Getting Scilab and the Programs -- Appendix A. List of Symbols -- Bibliography -- Index
Chapter 4. Direct Optimization Formulations --
Chapter 2. Failure Detection --
Contents --
Chapter 1. Introduction --
Appendix A. List of Symbols --
Preface --
Chapter 5. Remaining Problems and Extensions --
Chapter 6. Scilab Programs --
Frontmatter --
Index
Bibliography --
Chapter 3. Multimodel Formulation --