| 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 |
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| 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 |
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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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