电子测量与仪器学报

张庆一,景博,焦晓璇,王生龙,鲍杰,龙迎利,窦雯.基于 Entropy-SKF 的机载氧气浓缩器退化分析[J].电子测量与仪器学报,2021,35(9):49-57

基于 Entropy-SKF 的机载氧气浓缩器退化分析

Degradation stage identification of airborne oxygenconcentrator based on Entropy-SKF

DOI：

英文关键词:oxygen concentrator Shannon entropy Kalman filtering stage identification

基金项目:国防科技创新特区项目（193 M06 001 002 05）、航空科学基金（20200033096001）、空军预先研究项目（3030507 2）资助

作者	单位
张庆一	1. 空军工程大学航空工程学院
景博	1. 空军工程大学航空工程学院
焦晓璇	1. 空军工程大学航空工程学院
王生龙	1. 空军工程大学航空工程学院
鲍杰	1. 空军工程大学航空工程学院
龙迎利	2. 威海技师学院
窦雯	3. 空军成都代表局

Author	Institution
Zhang Qingyi	1. College of Aeronautics Engineering, Air Force Engineering University
Jing Bo	1. College of Aeronautics Engineering, Air Force Engineering University
Jiao Xiaoxuan	1. College of Aeronautics Engineering, Air Force Engineering University
Wang Shenglong	1. College of Aeronautics Engineering, Air Force Engineering University
Bao Jie	1. College of Aeronautics Engineering, Air Force Engineering University
Long Yingli	2. Weihai Technician College
Dou Wen	3. Chengdu Representative Office of Air Force

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中文摘要:

基于分子筛床变压吸附原理的机载氧气浓缩器是飞机生命保障系统的核心部件,可以为飞行员在飞行过程中提供氧气。对机载氧气浓缩器进行退化分析,可以实现故障预警,对机载氧气浓缩器的视情维修和构建飞机健康管理系统具有重要意义。机载氧气浓缩器的退化过程可以分为平稳阶段和加速退化两个阶段,但是由于退化模式变化点的不确定性,导致了退化模式转变的不确定性,因此正确识别退化模式转折点十分重要。氧分压值是反映机载氧气浓缩器制氧能力的一个重要参数,利用数据驱动的方法,针对模式转换的模糊性,首先提取数据的香农熵,然后通过转换卡尔曼(SKF)滤波器对实时数据样本进行处理,根据稳态退化和加速退化两个滤波器之间的后验概率来识别当前的退化模式,识别结果与实际情况相符,最后与小波分解和 K-means 算法进行对比,证明了基于信息熵的转换卡尔曼滤波器(Entropy-SKF)算法的有效性。

英文摘要:

Airborne oxygen concentrator based on molecular sieve bed pressure swing adsorption principle is the core component of aircraft life support system, which can provide oxygen for pilots during flight. The degradation analysis of airborne oxygen concentrator can realize fault early warning, which is of great significance for the condition based maintenance of airborne oxygen concentrator and the construction of aircraft health management system. The degradation process of airborne oxygen concentrator can be divided into two stages: steady stage and accelerated degradation stage. However, due to the uncertainty of degradation mode change point, the transformation of degradation mode is uncertain. Therefore, it is very important to correctly identify the turning point of degradation mode. Oxygen partial pressure is an important parameter to reflect the oxygen production capacity of airborne oxygen concentrator. In this paper, the Shannon entropy of the data is extracted by using the data-driven method. Then the SKF filter is used to process the real-time data samples. The current degradation mode is identified according to the posterior probability between the steady-state degradation filter and the accelerated degradation filter, the recognition results are consistent with the actual situation. Finally, compared with wavelet decomposition and K-means algorithm, the effectiveness of Entropy-SKF algorithm is proved.

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