Chapter 11. Change Detection Based on Algebraical Consistency Tests

  1. Fredrik Gustafsson

Published Online: 16 OCT 2001

DOI: 10.1002/0470841613.ch11

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

How to Cite

Gustafsson, F. (2001) Change Detection Based on Algebraical Consistency Tests, in Adaptive Filtering and Change Detection, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/0470841613.ch11

Author Information

  1. Linkoping University, Linkoping, Sweden

Publication History

  1. Published Online: 16 OCT 2001

ISBN Information

Print ISBN: 9780471492870

Online ISBN: 9780470841617

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Keywords:

  • data segmentation;
  • change detection;
  • jump Markov model;
  • piecewise linear regression model;
  • Matlab;
  • Matlab toolbox;
  • autotuning;
  • signal processing;
  • adaptive signal processing;
  • adaptive systems;
  • fault diagnosis;
  • monitoring;
  • surveillance;
  • signal detection;
  • decision making;
  • statistics;
  • stochastic processes;
  • modeling;
  • estimation;
  • identification;
  • parameter estimation;
  • state estimation;
  • observers;
  • stochastic systems;
  • time-varying systems;
  • discrete time systems;
  • sampled data systems;
  • filters;
  • communication systems;
  • adaptive control

Summary

Algebraic approaches to fault detection based on linear state space models with additive faults are described. These include observers and parity spaces, with the aim of decoupling dynamics and disturbances from the faults using analytical redundancy, enabling a solution to the fault isolation problem in diagnosis. Applications to a lab DC motor and aircraft dynamics are presented, and the sensitivity and robustness of this approach evaluated and compared to statistical methods.