CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System

• URL: http://arxiv.org/abs/2406.17788v1
• Date: Mon, 27 May 2024 08:49:47 GMT
• Title: CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System
• Authors: Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini,
• Abstract summary: In Vapor Cycle Systems, the mass flow sensor plays a key role for different monitoring and control purposes. The conception of a virtual sensor, based on other standard sensors, is a good alternative.
• Score: 9.007685592514187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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