Citation

He Y, Cao Z. China Saf. Sci. J. 2022; 32(2): 107-114.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.02.015

PMID

unavailable

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. © 2022, Editorial Department of China Safety Science Journal. All rights reserved.

Language: zh