基于粒子群算法的SVM飞机空调系统状态评估

飞机空调系统对飞机和旅客都起着至关重要的作用,对飞机QAR(Quick Access Recorder)空调数据的健康评价进行预测,可以保证乘客和机组的飞行舒适性、安全性,以及电子电气设备工作的稳定性,避免机械故障导致的航班延误或取消。为提高空调系统状态监控SVM模型预测的准确度,提出了一种基于粒子群算法的SVM空调状态评估方法。通过实验结果可知,使用A320飞机空调系统状态监控收集的样本数据进行预测分析,提出的方法能够有效评估空调系统状态。

State Evaluation of Aircraft Air Conditioning System Based on SVM of Particle Swarm Algorithm

Aircraft air-conditioning system plays a vital role for both aircraft and passengers. Predicting the health evaluation of aircraft QAR (Quick Access Recorder) air-conditioning data can ensure the flight comfort and safety of passengers and crew, and can also improve equipment work stability to avoid flight delays or cancellations caused by equipment failures. In order to improve the prediction accuracy of the SVM model of air-conditioning system state monitoring, an SVM air-conditioning state evaluation method based on the particle swarm algorithm is proposed. The experimental results show that the proposed method can effectively evaluate the state of the air-conditioning system by using the sample data collected by the A320 aircraft air-conditioning system state monitoring to conduct predictive analysis.