Acta Metrologica Sinica  2020, Vol. 41 Issue (11): 1443-1448    DOI: 10.3969/j.issn.1000-1158.2020.11.21
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Pressure Compensation Method on Aircraft Cabin Air Quality Detection
HE Yong-bo,TIAN Ji-lei,HUANG Lü-lin,LI Ming-wei
School of Electronic Information and Automation,Civil Aviation University of China,Tianjin,300300,China
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Abstract  The larger aircraft cabin air pressure range had great influence on the gas sensor, resulting in inaccurate air quality detect results, RBF neural network was proposed to compensate air pressure. Firstly, the experimental system was designed. Then positive and negative pressure experiments were carried out on four typical gas sensors for cabin air quality detection including HCHO, CO, CO2 and NO2. The test data were collected and the characteristic curves of each gas were drawn. Finally, a three-layer RBF neural network model with 12 pressure points and measured values as inputs and expected values as outputs was established, and the error correction compensation was made to the experimental data. The results showed that the RBF neural network compensation algorithm can reduce the maximum relative error of HCHO, CO, CO2 and NO2 gas sensors from 32.85%, 28.42%, 52.87%, 87.18% to 2.001%, 3.668%, 2.392%, 12.68% respectively, achieve a better compensation effect.
Key words： metrology      air quality detection      air pressure compensation      aircraft cabin      gas sensor      RBF neural network     
Received: 19 January 2020      Published: 02 November 2020
PACS:  TB99  
  X851  
Fund:Project supported by the Science and Technology Program of Civil Aviation Administration ,China：Study on airworthiness certification technology of cockpit air environment of large passenger aircraft
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HE Yong-bo
TIAN Ji-lei
HUANG Lü-lin
LI Ming-wei
Cite this article:   
HE Yong-bo,TIAN Ji-lei,HUANG Lü-lin, et al. Pressure Compensation Method on Aircraft Cabin Air Quality Detection[J]. Acta Metrologica Sinica, 2020, 41(11): 1443-1448.
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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.11.21     OR     http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I11/1443
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