Applying Evolving Fuzzy Models with Adaptive Local Error Bars to On-Line Fault Detection
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The main contribution of this talk 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
The evolving fuzzy models represent (changing) non-linear dependencies between certain system variables and are used for
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.