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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 control systems is a mechanism for failure detection to insure safety and reliability. This book offers an alternative failure detection approach that addresses two of the fundamental problems in the safe and efficient operation of modern control systems: failure detection--deciding when a failure has occurred--and model identification--deciding which kind of failure has occurred. Much of the work in both categories has been based on statistical methods and under the assumption that a given system was monitored passively. Campbell and Nikoukhah's book proposes an "active" multimodel approach. It calls for applying an auxiliary signal that will affect the output so that it can be used to easily determine if there has been a failure and what type of failure it is. This auxiliary signal must be kept small, and often brief in duration, in order not to interfere with system performance and to ensure timely detection of the failure. The approach is robust and uses tools from robust control theory. Unlike some approaches, it is applicable to complex systems. The authors present the theory in a rigorous and intuitive manner and provide practical algorithms for implementation of the procedures.

System failures (Engineering) --- Fault location (Engineering) --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Location of system faults --- System fault location (Engineering) --- Dynamic testing --- Failure of engineering systems --- Reliability (Engineering) --- Systems engineering --- A priori estimate. --- AIXI. --- Abuse of notation. --- Accuracy and precision. --- Additive white Gaussian noise. --- Algorithm. --- Approximation. --- Asymptotic analysis. --- Bisection method. --- Boundary value problem. --- Calculation. --- Catastrophic failure. --- Combination. --- Computation. --- Condition number. --- Continuous function. --- Control theory. --- Control variable. --- Decision theory. --- Derivative. --- Detection. --- Deterministic system. --- Diagram (category theory). --- Differential equation. --- Discrete time and continuous time. --- Discretization. --- Dynamic programming. --- Engineering design process. --- Engineering. --- Equation. --- Error message. --- Estimation theory. --- Estimation. --- Finite difference. --- Gain scheduling. --- Inequality (mathematics). --- Initial condition. --- Integrator. --- Invertible matrix. --- Laplace transform. --- Least squares. --- Likelihood function. --- Likelihood-ratio test. --- Limit point. --- Linear programming. --- Linearization. --- Mathematical optimization. --- Mathematical problem. --- Maxima and minima. --- Measurement. --- Method of lines. --- Monotonic function. --- Noise power. --- Nonlinear control. --- Nonlinear programming. --- Norm (mathematics). --- Numerical analysis. --- Numerical control. --- Numerical integration. --- Observational error. --- Open problem. --- Optimal control. --- Optimization problem. --- Parameter. --- Partial differential equation. --- Piecewise. --- Pointwise. --- Prediction. --- Probability. --- Random variable. --- Realizability. --- Remedial action. --- Requirement. --- Rewriting. --- Riccati equation. --- Runge–Kutta methods. --- Sampled data systems. --- Sampling (signal processing). --- Scientific notation. --- Scilab. --- Shift operator. --- Signal (electrical engineering). --- Sine wave. --- Solver. --- Special case. --- Stochastic Modeling. --- Stochastic calculus. --- Stochastic interpretation. --- Stochastic process. --- Stochastic. --- Theorem. --- Time complexity. --- Time-invariant system. --- Trade-off. --- Transfer function. --- Transient response. --- Uncertainty. --- Utilization. --- Variable (mathematics). --- Variance.