【摘要】 振动信号处理技术能够提取反映结构健康的特征信息,在导管架平台健康监测中发挥了重要作用。为了给导管架平台健康监测与振动特征提取的发展提供借鉴和指导,文中对导管架平台特征提取中常用的信号处理方法:傅里叶变换、小波变换和希尔伯特-黄(Hilbert-Huang)变换进行了综述,系统总结了它们的基本理论、适用范围、优缺点以及在导管架平台健康监测中的应用情况。文中发现傅里叶变换方法简单、适用性强,对于平稳信号有很好的处理效果;小波变换和希尔伯特-黄变换能很好地提取非平稳信号的时频特性,在导管架平台健康监测与特征提取中应用广泛。同时,随着人工智能算法的快速发展,未来的发展中,可以进行信号处理方法与人工智能算法的结合使用,以期获得更好的信号处理效果。更多还原

【Abstract】 Vibration signal processing technologies can extract features reflecting the structural health, which play an important role in the health monitoring of jacket platforms. To provide reference and guidance for the development of jacket platform health monitoring and feature extraction, signal processing methods including Fourier Transform, Wavelet Transform and Hilbert-Huang Transform, which are commonly used in feature extraction of jacket platforms, are reviewed in this paper. Their basic theories, advantages and disadvantages, and their applications in health monitoring of jacket platforms are systematically summarized. It is found that Fourier Transform is simple and suitable for stationary signals analyzing. Wavelet Transform and Hilbert-Huang Transform can extract the time-frequency characteristics of non-stationary signals, which is widely used in the health monitoring and feature extraction of jacket platform. With the rapid development of artificial intelligence(AI) algorithms, signal processing methods mentioned in this paper can be combined with artificial intelligence algorithms to obtain better signal processing results in future development.更多还原