CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914849089388544 |
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| author | Reverdi, Justin Zhang, Sixin Aoues, Saïd Gamboa, Fabrice Gratton, Serge Pellegrini, Thomas |
| author_facet | Reverdi, Justin Zhang, Sixin Aoues, Saïd Gamboa, Fabrice Gratton, Serge Pellegrini, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System Reverdi, Justin Zhang, Sixin Aoues, Saïd Gamboa, Fabrice Gratton, Serge Pellegrini, Thomas Systems and Control Machine Learning Signal Processing 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. |
| title | CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System |
| topic | Systems and Control Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2406.17788 |