哈圣,徐昊,唐震,朱赤洲.基于高斯混合模型的发动机稳态数据特征值提取方法[J].航空发动机,2022,48(5):173-179
基于高斯混合模型的发动机稳态数据特征值提取方法
Eigenvalue Extraction Method for Engine Steady-state Data Based on Gaussian Mixture Model
  
DOI:
中文关键词:  稳态数据  特征值提取  噪声  高斯混合模型  航空发动机
英文关键词:steady-state data  eigenvalue extraction  noise  Gaussian mixture model  aeroengine
基金项目:航空动力基础研究项目资助
作者单位E-mail
哈圣,徐昊,唐震,朱赤洲 中国航发沈阳发动机研究所沈阳110015 HS_Smith@163.com 
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中文摘要:
      因航空发动机的工作环境及工作特性所致,其稳态试验数据常伴有噪声干扰,对稳态值计算结果的准确性造成影响。 在对稳态数据进行正态性检验后,利用稳态数据来源的正态特性,以及利用混合模型对数据的良好回归特性,基于高斯混合模型 对稳态数据进行筛选分类。依托发动机稳态数据分布形式的相近性与数据本身的统计特性,来确定稳态数据特征值的提取方法。 在对发动机稳态数据进行数据筛选以及对比不同稳态数据片段后,验证模型方法的数据降噪效果以及稳态数据特征值计算结果 与同一稳定工作状态数据片段的选取不相关性。结果表明:从仿真数据的筛选结果以及不同稳态数据片段的验证结果可知,该方 法具有较强的稳定性,可有效筛选出发动机稳态点数据,并准确计算出发动机稳态值,一般收敛结果相对误差在0.2%以内。
英文摘要:
      Due to the working environment and characteristics of aeroengine,its steady-state test data were often accompanied by noise interference,which affected the accuracy of steady-state value calculation results. After the normality test of the steady-state data, the Gaussian mixture model was used to screen and classify the steady-state data based on the normal characteristics of the steady-state da? ta source and the good regression characteristics of the mixed model. Based on the similarity of the distribution form of engine steady-state data and the statistical characteristics of the data itself,the extraction method of steady-state data eigenvalues was determined. After data screening of engine steady-state data and comparison of different steady-state data segments,the data noise reduction effect of the model method and the calculation results of steady-state data eigenvalues were verified to be independent to the segment selection of the same steady-state data. The results show that from the screening results of simulation data and the verification results of different steady-state da? ta segments,the method has strong stability,which can effectively screen the steady-state point data of the engine and accurately calculate the steady-state value of the engine. Generally,the relative error of the convergence result is within 0.2%.
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