飞机座舱空气质量检测气压补偿方法

飞机座舱气压变化范围较大,对气体传感器产生较大影响,导致空气质量检测结果不准确,提出采用RBF神经网络进行气压补偿。首先设计试验系统;然后对HCHO、CO、CO2和NO2共4种典型的座舱空气质量检测气体传感器进行正负压试验,采集试验数据并绘制各气体的特征变化曲线;最后建立了以12个气压点和测量值为输入、期望值为输出的3层RBF神经网络模型,并对试验数据进行了误差修正补偿。结果表明:采用该RBF神经网络补偿算法,HCHO、CO、CO2、NO2气体传感器的最大相对误差分别由32.85%、28.42%、52.87%、87.18%降低到2.001%、3.668%、2.392%、12.68%,达到较好的补偿效果。

Pressure Compensation Method on Aircraft Cabin Air Quality Detection

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.