HHT统计特征在航空发动机振动分析中的应用

航空发动机振动信号包含了多种振源的振动信号和大量的噪声分量。通过对其进行Hilbert-Huang变换, 将复杂信号分解为代表不同物理意义的单分量固有模态函数（Intrinsic Mode Functions, IMF），然后对每一个IMF子带信号提取基于能量加权的广义粗糙度特征实现对振动信号的描述。最后将上述特征送入SVM分类器进行训练，根据分类器的输出结果确定航空发动机的工作状态和故障类型。通过对实测航空发动机试车时得到的振动信号的实验分析结果表明，该算法可以有效地识别发动机的振动故障。

The Statistical Feature of the Hilbert-Huang Transform and Its Application on Aeroengine Vibration Analysis

The vibration signals of the aeroengine compose of multi-frequency components as well as an abundant of noise. The Hilbert-Huang transform is used to decompose the vibration signals into a number of intrinsic mode functions (IMF) which were the physically meaningful representations of the data. Then, the generalized roughness vector of the IMF which is weighted by its energy was got. Finally, the desired parameters serve as the fault characteristic vectors to be input to the support vector machine classifier and the work conditions and fault patterns are identified by the output of the classifier. The analysis results from the aeroengine vibration signals show that the fault diagnosis method can classify working conditions and fault patterns effectively.