基于HHT的船体结构应力监测数据特征分析和去噪方法

Characteristic analysis and de-noising method of stress monitoring data of hull structures based on HHT

    摘要
  • 摘要:
      目的  为了去除船体结构应力监测数据中的噪声信号，获得有效的数据信息，以便为后续数据挖掘提供支撑，
      方法  首先，采用HHT方法中的经验模态分解（EMD）算法对数据进行成分分析，得到固有模态函数（IMF）和余项。然后，通过Hilbert变换得到Hilbert谱，证明应力监测数据的非平稳特性。最后，以信噪比（SNR）和均方根误差（RMSE）为例，结合自适应去噪和小波阈值去噪两种方法对应力监测数据进行去噪效果比较。
      结果  结果表明，基于HHT方法的自适应去噪和小波去噪都具有一定的去噪效果，但两种去噪方法中，自适应去噪方法的SNR更高，RMSE更小，自适应去噪方法性能最佳。
      结论  研究证明自适应去噪方法能更有效地针对应力监测数据进行去噪处理。

     

    Abstract:
      Objectives  In order to remove the noise signal from the hull stress monitoring data and obtain effective data information to provide support for further data mining,
      Methods  a component analysis of data by using the Empirical Mode Decomposition(EMD) in Hilbert-Huang Transform(HHT) method was carried out firstly in this paper to get the Intrinsic Mode Function(IMF) and the remainder. Then the Hilbert spectrum was obtained by Hilbert transform to prove the non-stationary characteristics of the stress monitoring data. Finally, taking Signal-Noise-Ratio(SNR)and Root Mean Square Error(RMSE) as examples and combining the adaptive de-noising and wavelet threshold de-noising methods, the de-noising effect of stress monitoring data was compared and verified.
      Results  The results show that the two methods based on HHT have certain de-noising effect. Among them, the adaptive de-noising method has bigger SNR and smaller RMSE. Above all, the adaptive de-noising method has the best performance.
      Conclusions  The study proves that the adaptive de-noising method can de-noise the stress monitoring data more effectively.

     

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