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

  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.