Changing operating condition may require updates for the
dynamic models. Recursive updates are needed when
there are not sufficient information about the new situations.
In machine diagnostics and prognostics, the analysis
starts from good conditions and new phenomena,
which activate with time, may change considerably the
model. In biological wastewater treatment processes, the
condition of the biomass changes drastically the dynamic
operation of the treatment process. Direct measurements
of the biomass condition are under development. Recursive
modelling is clearly needed in these situations. The
usual approachis to modify the model equations. However,
the interactions do not necessarily change if the
meanings of the variables are modified. This paper keeps
the the model equations constant and modifies the nonlinear
scaling of the variables by extending the data-driven
scaling to recursive approach. The recursive methodology
is tested in two applications: machine diagnostics and
wastewater treatment.
intelligent modelling; recursive statistical analysis; adaptive modelling; prognostics; transitions
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