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摘要:
耦合应力条件下的建模是故障预测与健康管理领域的难点问题。以氧气浓缩器地面试验退化建模为例,针对试验中2种应力线性相关且耦合作用于氧气浓缩器退化的问题,提出了一种机理模型与数据驱动联合的偏微分方程建模方法。基于退化机理分析建立偏微分方程的基本形式,利用数据驱动的方法确定方程具体参数。通过偏微分方程建模,对2种应力进行解耦分析,确定引气湿度的增加会加快氧气浓缩器的退化速率,发现随着氧气浓缩器工作性能的退化,氧气浓缩器氧分压对引气压力的敏感性减弱,确定氧分压随引气压力变化斜率为健康因子。通过卡尔曼滤波器模式识别,确定氧气浓缩器退化可分为平稳阶段与退化阶段,与实际服役环境下氧气浓缩器退化数据对比,验证了氧气浓缩器两阶段退化特性。
Abstract:Modeling under coupling stress is challenge in the field of fault prediction and health management. Based on the the ground degradation test of the oxygen concentrator, this research proposes a modeling framework with partial differential equations combining mechanism model and data driven model, which addresses the linear correlation of two stresses during the test and their coupling effect on the degradation. Based on the analysis of degradation mechanism of the basic form, data driven method is used to determine the specific parameter of partial differential equations. Then, the two stresses are decoupled based on the equations. Results show that the increase in the bleed air moisture content raises the degradation rate of the oxygen concentrator. Furthermore, as the oxygen concentrator performance degrades, the sensitivity of the oxygen partial pressure on the bleed air pressure is reduced. Therefore, the slope factor of the oxygen partial pressure to bleed air pressure is determined as the health indicator. The pattern recognition of Kalman filter shows that the oxygen concentrator degradation can be divided into stationary and degradation stages, which is verified by comparison with the degradation data of the oxygen concentrator in the actual service environment.
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表 1 氧气浓缩器退化试验数据记录
Table 1. Oxygen concentrator degradation test data
引气压力/MPa 引气湿度/(g·L−1) 引气温度/℃ 氧浓度/% 氧分压/kPa 0.14 32.84 70 38.1 34.9 0.3 61.78 69 59.8 57.5 0.7 80.63 69 63.9 61.8 1.0 94.47 71 64.3 63.5 0.7 74.38 71 65 63.3 0.3 54.00 69 64.4 62.4 0.14 34.02 69 38.3 37.9 表 2 健康因子性能指标
Table 2. Health factor performance indicators
指标 HI 本文 0.14MPa 0.3MPa 0.7MPa 1MPa Corr 0.968 0.718 0.852 0.903 0.877 Mon 0.796 0.582 0.603 0.616 0.573 -
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