Filter Design for System Modeling, State Estimation and Fault Diagnosis analyzes the latest methods in the design of filters for system modeling, state estimation and fault detection with the intention of providing a new perspective of both theoretical and practical aspects.
This book also includes fault diagnosis techniques for unknown but bounded systems, their real applications on modeling and fault diagnosis for lithium battery systems, DC-DC converters and spring damping systems. It proposes new methods based on zonotopic Kalman filtering, a variety of state estimation methods of zonotope and its derived algorithms, a state estimation method based on convex space, set inversion interval observer filtering-based guaranteed fault estimation and a novel interval observer filtering-based fault diagnosis.
The methods presented in this text are more practical than the common probabilistic-based algorithms, since these can be applied in unknown but bounded noisy environments. This book will be an essential read for students, scholars and engineering professionals who are interested in filter design, system modeling, state estimation, fault diagnosis and related fields.
Author(s): Ziyun Wang, Yan Wang, Zhicheng Ji
Publisher: CRC Press
Year: 2022
Language: English
Pages: 238
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Symbol Description
1. Introduction
1.1. System modeling
1.1.1. Background
1.1.2. Methods of system modeling
1.2. State estimation
1.3. Fault diagnosis
1.4. Summary of filtering design methods
1.4.1. Traditional filtering design methods
1.4.2. Non-probabilistic filtering design method
1.5. Motivation and objective
1.6. Outlines
2. Parameter estimation algorithm based on zonotope-ellipsoid double filtering
2.1. Problem description
2.1.1. Model parameterization
2.1.2. Symbol definitions
2.2. Main results
2.2.1. Ellipsoid-filtering-based estimation algorithm
2.2.1.1. Prediction step
2.2.1.2. Update step
2.2.2. Zonotopic dimensional reduction filtering
2.2.3. Discretization of zonotope into constraint strips
2.3. Numerical examples
2.4. Conclusions
3. State estimation based on zonotope
3.1. Set-membership filtering based bi-directional DC-DC converter state estimation algorithm for lithium battery formation
3.1.1. Problem description
3.1.1.1. Buck mode
3.1.1.2. Boost mode
3.1.2. Preliminaries
3.1.2.1. Definition of strip and zonotope
3.1.2.2. Properties
3.1.3. Main results
3.1.3.1. Prediction
3.1.3.2. Update
3.1.4. Simulation results
3.1.4.1. Simulation of buck mode
3.1.4.2. Simulation of boost mode
3.1.5. Conclusion
3.2. A novel set-valued observer based state estimation algorithm for nonlinear systems
3.2.1. System description
3.2.2. Central difference zonotopic set-valued observer
3.2.2.1. Nonlinear model linearization
3.2.2.2. Bounded linearization error
3.2.2.3. Time update
3.2.2.4. Observation update
3.2.3. Numerical examples
3.2.4. Conclusions
3.3. Zonotopic particle filtering based state estimation algorithm and its application on temperature recognition for lithium battery
3.3.1. Problem equation and preliminaries
3.3.1.1. Problem formulation
3.3.1.2. Preliminaries
3.3.2. Zonotopic particle filtering based state estimation algorithm
3.3.2.1. Prediction step
3.3.2.2. Update step
3.3.3. Simulation
3.3.4. Conclusion
4. State estimation based on convex spatial structure
4.1. Hyperparallel space set-membership filtering based state estimation algorithm for nonlinear system
4.1.1. Problem description
4.1.2. Preknowledge
4.1.2.1. Parallelotope and orthotope
4.1.2.2. Property
4.1.3. Nonlinear set-membership filtering based on parallelotope
4.1.3.1. Outer bound of linearization error
4.1.3.2. Predictive step
4.1.3.3. Update step
4.1.4. Simulation
4.1.5. Conclusion
4.2. Nonlinear system state estimation based on axisymmetric box space filter under uncertain noise
4.2.1. Related definitions and problem descriptions
4.2.1.1. Related definitions
4.2.1.2. Problem description
4.2.2. State estimation of nonlinear system based on axisymmetric box space filtering
4.2.2.1. Linearization of nonlinear models
4.2.2.2. Interval expression of linearization error
4.2.2.3. State prediction
4.2.2.4. Measurement update
4.2.3. Simulation
4.2.4. Conclusion
5. Fault diagnosis based on interval
5.1. Guaranteed fault-estimation algorithm based on interval set inversion observer filtering
5.1.1. Preliminaries and problem description
5.1.2. Main results
5.1.2.1. Minimal conservative interval observer
5.1.2.2. Dimensional vector set inversion interval contraction
5.1.2.3. Algorithm analysis
5.1.3. Simulation analysis
5.1.3.1. Numerical simulation
5.1.3.2. DC motor system simulation
5.1.4. Conclusions
5.2. Interval observer filtering-based fault diagnosis method for linear discrete-time systems with dual uncertainties
5.2.1. Problem description
5.2.2. Main results
5.2.2.1. State estimator
5.2.2.2. Fault diagnosis
5.2.3. Simulation analysis
5.2.3.1. Numerical example
5.2.3.2. Fault-free case
5.2.3.3. Fault detection, isolation and identification
5.2.3.4. DC motor system simulation
5.2.3.5. DC motor working normally
5.2.3.6. DC motor fails
5.2.4. Conclusions
5.3. Orthometric hyperparallel spatial directional expansion filtering based fault diagnosis method
5.3.1. Pre-knowledge
5.3.2. Problem description
5.3.3. Orthometric hyperparallel spatial directional expansion filtering based fault diagnosis method
5.3.3.1. Fault detection
5.3.3.2. Fault isolation and identification
5.3.4. Simulation
5.3.5. Conclusion
6. Fault diagnosis method based on zonotopic Kalman filtering
6.1. Zonotopic Kalman filtering-based fault diagnosis algorithm for linear system with state constraints
6.1.1. Problem formulation and preliminaries
6.1.1.1. Problem formulation
6.1.1.2. Preliminaries
6.1.2. Main results
6.1.2.1. Prediction step
6.1.2.2. Update step
6.1.2.3. Fault diagnosis
6.1.3. Simulation
6.1.3.1. Fault-free system simulation
6.1.3.2. Faulty system simulation
6.1.4. Conclusion
6.2. Sensor fault estimation based on the constrained zonotopic Kalman filter
6.2.1. Preliminaries
6.2.2. Problem formulation
6.2.3. Main results
6.2.3.1. Design of the constrained zonotopic Kalman filter
6.2.3.2. Fault detection
6.2.3.3. Design of constrained zonotopic Kalman filter based fault estimator
6.2.4. Simulation analysis
6.2.4.1. Fault-free
6.2.4.2. Additive sensor fault
6.2.4.3. Multiplicative sensor fault
6.2.4.4. Fault-free state
6.2.4.5. Additive sensor fault
6.2.4.6. Multiplicative sensor fault
6.2.5. Conclusion
6.3. Optimal zonotopic Kalman filter-based state estimation and fault diagnosis algorithm for linear discrete-time system with time delay
6.3.1. Problem formulation and preliminaries
6.3.2. Design of the optimal ZKF for the system with time delay
6.3.3. Fault diagnosis
6.3.3.1. Fault detection
6.3.3.2. Fault identification
6.3.4. Simulation analysis
6.3.4.1. Fault-free
6.3.4.2. Fault in the fault library
6.3.4.3. Fault-free
6.3.4.4. Fault outside the fault library
6.3.5. Conclusion
7. Summary
References
