摘要
参数不确定度的分析及合成是动力装置性能试飞中最为关键的环节之一,而参数名义值的获取以及剔除异常数据是不确定度分析的基础。本文介绍了一种基于均值迭代的稳态数据处理算法,详细分析了该方法的工作原理、名义值求取步骤。算例计算表明,本方法对数据样本的概率分布无要求,适用性强,避免了一些传统算法的复杂计算,而且可以实时地评估稳定段数据的抖动程度,适合于参数样本分布未知的航空发动机关键截面参数的数据预处理。
The analysis and synthesis of parameter uncertainty is one of the most important procedures in the performance test of power plant, for which the acquisition of the nominal value of parameters and the elimination of abnormal data are basis. In this paper, a method of steady state data processing based on mean iteration was introduced include a detailed analysis of the working principle of the method and the steps of obtaining the nominal value. Calculation shows that, the method has strong applicability because it not only has no requirement for the probability distribution of data sample, but also avoids the complex calculation in some traditional algorithms. Furthermore, data jitter in stable period could be evaluated real-time so that it could be more suitable for critical aeroengine section parameter data preprocessing in which the parameters distribution is unknown.
出处
《航空科学技术》
2016年第7期48-52,共5页
Aeronautical Science & Technology
关键词
均值迭代
数据关联
稳态飞行数据
mean iteration
data correlation
steady state flight test data
