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.

本文提出了一个数据驱动模型，使用卷积神经网络来估计压缩机的质量流量，并展示了它在标准均方误差和工程师性能指标方面明显优于标准多项式回归模型。此外，还提出了一种半自动分割方法来计算真实数据集的工程师性能指标，因为标准均方误差指标在分析蒸汽循环系统的动态行为时可能存在风险。