Detection of abrupt changes: theory and application
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Summary
A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.
- Type
- article
- Published
- 1993-03-01
- Cited by
- 4,127
- References
- 218
- OpenAlex
- https://openalex.org/W2044535354
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:121394317
Keywords
Computer science, Redundancy (engineering), Change detection, Process (computing), Fault detection and isolation
References
- Nonlinear Programming: Theory, Algorithms and Applications
- A Review of Parity Space Approaches to Fault Diagnosis
- Corrected Diffusion Approximations and Their Applications.
- Analytical Redundancy Methods in Fault Detection and Isolation
- Nonparametric Methods in Change Point Problems
- Cumulative Sum Techniques
- Detection of Abrupt Changes in Signals and Dynamical Systems
- Two self-test methods applied to an inertial system problem. [estimating gyroscope and accelerometer bias
- Control System Synthesis : A Factorization Approach
- SEQUENTIAL DETECTION OF CHANGES IN STOCHASTIC SYSTEMS
- Robust Observer Design with Application to Fault Detection
- Failure detection in linear systems
- Detection Filter Design: Spectral Theory and Algorithms
- The Economic Design of Control Charts: A Review and Literature Survey
- Optimal Segmentation of Linear Regression Parameters
- Fault diagnosis in dynamic systems: theory and application
- Asymptotic Methods In Statistical Decision Theory
- Application of the Generalized Likelihood Ratio Algorithm to Maneuver Detection and Estimation
- Detection of changes in random processes
- Principles and practice of information theory
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- Sound Interaction by use of Comparative Visual Displays
- An automatic multi-lead electrocardiogram segmentation algorithm based on abrupt change detection
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- Estimation of Parameters and Arrival Instants of a Signal in a Color Noise Background
- Model selection via cross-validation in density estimation, regression, and change-points detection
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