  In Vapor Cycle Systems, the mass flow sensor playsa key role for different
monitoring and control purposes. However,physical sensors can be inaccurate,
heavy, cumbersome, expensive orhighly sensitive to vibrations, which is
especially problematic whenembedded into an aircraft. The conception of a
virtual sensor, basedon other standard sensors, is a good alternative. This
paper has twomain objectives. Firstly, a data-driven model using a
ConvolutionalNeural Network is proposed to estimate the mass flow of
thecompressor. We show that it significantly outperforms the standardPolynomial
Regression model (thermodynamic maps), in terms of thestandard MSE metric and
Engineer Performance metrics. Secondly,a semi-automatic segmentation method is
proposed to compute theEngineer Performance metrics for real datasets, as the
standard MSEmetric may pose risks in analyzing the dynamic behavior of
VaporCycle Systems.
