Pressure compensation algorithm of fire gas sensors based on IPSO-BP model

doi:10.16265/j.cnki.issn1003-3033.2022.02.015

Abstract

Abstract:

In order to improve fire detection system of aircraft, volume fraction values of CO and CO₂ gas sensors under different air pressure were simulated and compared with the theoretical value by designing a set of experimental scheme, and a particle swarm optimization algorithm was proposed to dynamically adjust learning factor according to particle fitness value. Then, IPSO algorithm was used to find optimal initial weight and threshold of BP neural network which, after optimization, was employed to modify detection results of CO and CO₂ gas sensors to eliminate influence of air pressure on sensor data acquisition. The results show that after compensation by IPSO-BP algorithm, gas volume fraction values at the selected 27 pressure points are close to the fitted true value. Among them, the maximum measurement error of CO₂ gas sensor drops from 542×10^-4% to 0.1×10^-4% after pressure compensation, and that of CO gas sensor decreases from 15.7×10^-4% to 0.01×10^-4%. Compared with BP neural network pressure compensation model, the accuracy of IPSO-BP neural network model is significantly improved.

Key words: improved particle swarm optimization (IPSO), back propagation (BP) neural network, gas sensor, pressure compensation, aircraft cargo compartment fire

HE Yongbo, CAO Zhubing. Pressure compensation algorithm of fire gas sensors based on IPSO-BP model[J]. China Safety Science Journal, 2022, 32(2): 107-114.

Figures/Tables 8

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传感器名称	CO传感器			CO₂传感器
气压值/kPa	理论值	测量值	误差	理论值	测量值	误差
150	1 667	2 209	542	66.7	79.5	12.8
140	1 556	1 961	405	62.3	74.6	12.3
130	1 445	1 743	298	57.8	69.4	11.6
120	1 334	1 620	286	53.4	65.6	12.2
传感器名称	CO传感器			CO₂传感器
110	1 225	1 491	266	48.9	59.7	10.8
100	1 112	1 360	248	44.5	54.1	9.6
90	100	1 244	244	40.1	49.6	9.5
80	889	1 183	223	35.6	45.1	9.5
70	778	1 011	233	31.2	42.1	10.9
60	667	910	243	26.7	37.3	10.6
50	556	786	230	22.3	33.5	11.2
40	445	672	227	17.8	29.8	12
30	334	477	143	13.4	27.2	13.8
20	223	376	153	8.9	24.6	15.7

算法模型	误差平均值/CO	误差平均值/CO₂
BP神经网络	8.606 68	110.423 78
IPSO-BP神经网络	0.617 824	10.653 02