Application of Fault Diagnosis to Industrial Systems

  • Gerasimos G. Rigatos
Part of the Intelligent Systems Reference Library book series (ISRL, volume 7)

Abstract

Applications of statistical methods for fault diagnosis are presented. First, the problem of early diagnosis of cascading events in the electric power grid is considered. Residuals are generated with the use of a nonlinear model of the distributed electric power system and the fault threshold is determined with the use of the generalized likelihood ratio assuming that the residuals follow a Gaussian distribution. Next, the problem of fault detection and isolation in electric motors is analyzed. It is proposed to use nonlinear filters for the generation of residuals and to derive a fault threshold from the generalized likelihood ratio without prior knowledge of the residuals statistical distribution.

Keywords

Power System Kalman Filter Fault Diagnosis Particle Filter Electric Power System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gerasimos G. Rigatos

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