Edwin Lughofer, Carlos Guardiola,
"Applying Evolving Fuzzy Models with Adaptive Local Error Bars to On-Line Fault Detection"
: Proc. of the 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008, Seite(n) 35-40, 2008
Applying Evolving Fuzzy Models with Adaptive Local Error Bars to On-Line Fault Detection
Sprache des Titels:
Proc. of the 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008
The main contribution of this paper is a novel fault detection strategy, which is able to cope with
changing system states at on-line measurement systems fully automatically. For doing so, an improved fault detection logic is introduced which is based on data-driven evolving fuzzy models. These are sample-wise trained from online measurement data, i.e. the structure and rules of
the models evolve over time in order to cope 1.) with high-frequented measurement recordings and 2.) online changing operating conditions.
The evolving fuzzy models represent (changing) non-linear dependencies between certain system variables and are used for calculating the deviation between expected model outputs and real measured values on new incoming data samples (=> residuals).
The residuals are compared with local confidence regions surrounding the evolving fuzzy models, so-called
local error bars, incrementally calculated synchronously to the models. The behavior of the residuals is analyzed over time by an adaptive univariate statistical approach.