International Journal of Applied Mathematics and Computer Science (Jun 2018)

From Structural Analysis to Observer–Based Residual Generation for Fault Detection

  • Pröll Sebastian,
  • Lunze Jan,
  • Jarmolowitz Fabian

DOI
https://doi.org/10.2478/amcs-2018-0017
Journal volume & issue
Vol. 28, no. 2
pp. 233 – 245

Abstract

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This paper combines methods for the structural analysis of bipartite graphs with observer-based residual generation. The analysis of bipartite structure graphs leads to over-determined subsets of equations within a system model, which make it possible to compute residuals for fault detection. In observer-based diagnosis, by contrast, an observability analysis finds observable subsystems, for which residuals can be generated by state observers. This paper reveals a fundamental relationship between these two graph-theoretic approaches to diagnosability analysis and shows that for linear systems the structurally over-determined set of model equations equals the output connected part of the system. Moreover, a condition is proved which allows us to verify structural observability of a system by means of the corresponding bipartite graph. An important consequence of this result is a comprehensive approach to fault detection systems, which starts with finding the over-determined part of a given system by means of a bipartite structure graph and continues with designing an observerbased residual generator for the fault-detectable subsystem found in the first step.

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