| 基于经验模态分解和主分量分析的火箭发动机振动信号盲源分离 |
| Blind source separation of rocket engine vibration signals based on empirical mode decomposition and principal component analysis |
| 投稿时间：2024-12-18 修订日期：2024-12-31 |
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| 中文摘要: |
| 在火箭发动机试验中，发动机的振动数据是研究、分析、判断发动机和关键组件的工作状态的重要依据。振动数据中混入的噪声成分影响了研究人员对发动机工作状态的评估和性能的分析。针对发动机试验振动数据的非平稳非线性特征，提出了一种基于经验模态分解、主分量分析的去噪源分离方法（EMD－PCA－DSS），研究制定了该方法在工程应用中的实施步骤，并对发动机振动信号进行盲源分离。通过分析对比数据在分离前和分离后的频谱特性，结果显示EMD－PCA－DSS方法能够较好的分离出振动数据中的有价值信息，为研究人员进一步挖掘信号内部的有用信息，分析发动机振动数据提供了更好的依据。 |
| 英文摘要: |
| In rocket engine testing, the vibration data of the engine is an important basis for studying, analyzing, and judging the working status of the engine and key components. The noise components mixed in the vibration data affect the evaluation and performance analysis of the engine by researchers. A denoising source separation method based on empirical mode decomposition and principal component analysis is proposed to address the non-stationary and nonlinear characteristics of engine test vibration data. The implementation steps of this method in engineering applications are studied and developed, and blind source separation is performed on engine vibration signals. By analyzing and comparing the spectral characteristics of data before and after separation, the results show that the EMD-PCA-DSS method can effectively separate valuable information from vibration data, providing better basis for researchers to further explore useful information inside the signal and analyze engine vibration data. |
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| 中文关键词: 发动机 振动数据 经验模态分解 主分量分析 盲源分离 |
| 英文关键词:engine Vibration data Empirical Mode Decomposition Principal Component Analysis Blind source separation |
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