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Analytical redundancy and the design of robust failure detection systems

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TLDR
In this article, a robust failure detection and identification (FDI) process is viewed as consisting of two stages: residual generation and decision making, and it is argued that a robust FDI system can be achieved by designing a robust residual generation process.
Abstract
The failure detection and identification (FDI) process is viewed as consisting of two stages: residual generation and decision making. It is argued that a robust FDI system can be achieved by designing a robust residual generation process. Analytical redundancy, the basis for residual generation, is characterized in terms of a parity space. Using the concept of parity relations, residuals can be generated in a number of ways and the design of a robust residual generation process can be formulated as a minimax optimization problem. An example is included to illustrate this design methodology.

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Citations
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Journal ArticleDOI

Detection of abrupt changes: theory and application

TL;DR: 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.
Journal ArticleDOI

Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

Paul M. Frank
- 01 May 1990 - 
TL;DR: In this article, the authors review the state of the art of fault detection and isolation in automatic processes using analytical redundancy, and present some new results with emphasis on the latest attempts to achieve robustness with respect to modelling errors.
Journal ArticleDOI

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

TL;DR: This three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives and broadly classify fault diagnosis methods into three general categories and review them in three parts.
Book

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

TL;DR: This book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.
Journal ArticleDOI

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

TL;DR: The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade.
References
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Journal ArticleDOI

Paper: A survey of design methods for failure detection in dynamic systems

TL;DR: This paper surveys a number of methods for the detection of abrupt changes in stochastic dynamical systems, focusing on the class of linear systems, but the basic concepts carry over to other classes of systems.

A survey of design methods for failure detection in dynamic systems

TL;DR: A number of methods for detecting abrupt changes (such as failures) in stochastic dynamical systems are surveyed in this paper, where tradeoffs in complexity versus performance are discussed, ranging from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, and the development of jump process formulations.
Journal ArticleDOI

A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems

TL;DR: In this article, the authors consider a class of stochastic linear systems that are subject to jumps of unknown magnitudes in the state variables occurring at unknown times and devise an adaptive filtering system for the detection and estimation of the jumps.
Book

Failure detection in linear systems

TL;DR: Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1973 as discussed by the authors, Boston, Massachusetts, USA.