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基于统计分析与物理模型的批量小样本条件下作动器退化建模
引用本文:潘晋新,景 博,焦晓璇,王生龙,王可心. 基于统计分析与物理模型的批量小样本条件下作动器退化建模[J]. 仪器仪表学报, 2024, 45(4): 1-9
作者姓名:潘晋新  景 博  焦晓璇  王生龙  王可心
作者单位:1. 空军工程大学航空工程学院;2. 航空工业西安飞行自动控制研究所
基金项目:预研“十四五”共用技术(50902060401)项目资助
摘    要:作动器是飞行控制系统的重要组成,其性能直接影响飞行安全。 然而,大部分部件全寿命周期仅能获取 2 ~ 3 次的返所测试数据,性能退化样本极小,给作动器的性能预测带来困难。 针对这一问题,提出了一种统计分析与物理模型结合的性能预测方法,首先对批次型部件数据进行统计分析,建立作动器不同阶段的统计分布规律;然后,结合作动器退化的物理模型与统计规律,建立带有概率分布的作动器退化函数,并基于 AMESim 仿真对参数进行标定,得到不同时间与健康参数下的概率密度函数;最后,针对任一部件获取的健康参数,给出基于后验概率的概率密度函数更新方式。 为验证方法的有效性,本文采用多个含有 3 个数据点的样本进行验证,统计预测值精度。 结果表明,实测值在预测密度函数的 3sigma 范围的概率高达 92. 27% ,证明预测密度函数能够在高置信度下表征作动器退化规律。

关 键 词:小样本  退化建模  统计分析  作动器  AMESim 仿真

Actuator degradation modeling under batch small sample conditions based on statistical analysis and physical model
Pan Jinxin,Jing Bo,Jiao Xiaoxuan,Wang Shenglong,Wang Kexin. Actuator degradation modeling under batch small sample conditions based on statistical analysis and physical model[J]. Chinese Journal of Scientific Instrument, 2024, 45(4): 1-9
Authors:Pan Jinxin  Jing Bo  Jiao Xiaoxuan  Wang Shenglong  Wang Kexin
Affiliation:1. College of Aeronautics Engineering, Air Force Engineering University; 2. AVIC Xi′an Flight Automatic Control Research Institute,
Abstract:Actuators are essential components of flight control systems, and their performance directly affects flight safety. However, mostcomponents can only get the performance data 2~ 3 times during their life cycle, and the samples of performance degradation parametersare extremely small, which poses challenges for predicting actuator performance. To solve this problem, a performance prediction methodcombining statistical analysis and a physical model is proposed. Firstly, statistical analysis is carried out on the batch-type componentdata, and statistical distribution rules of different stages of the actuator are established. Then, based on the physical model and statisticallaw of the actuator degradation, a actuator degradation function with probability distribution is established. The function parameters arecalibrated based on AMESim simulation to obtain the probability density function under different time and health parameters. Finally, theupdate method of probability density function based on posterior probability is given for the health parameters obtained by anycomponent. To verify the effectiveness of the method, multiple samples containing 3 data points were used for validation. The resultsshow that the probability of the measured values in the 3σ range of the predicted density function is 92. 27% , which proves that thepredicted density function can characterize the degradation rule of actuators with high confidence.
Keywords:small sample   degradation modeling   statistical analysis   actuator   AMESim simulation
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