  We adopted an approach based on an LSTM neural network to monitor and detect
faults in industrial multivariate time series data. To validate the approach we
created a Modelica model of part of a real gasoil plant. By introducing hacks
into the logic of the Modelica model, we were able to generate both the roots
and causes of fault behavior in the plant. Having a self-consistent data set
with labeled faults, we used an LSTM architecture with a forecasting error
threshold to obtain precision and recall quality metrics. The dependency of the
quality metric on the threshold level is considered. An appropriate mechanism
such as "one handle" was introduced for filtering faults that are outside of
the plant operator field of interest.
