China
Africa
Explore chapters and articles related to this topic
On the assessment of roll damping for a damaged ferry
Published in Petar Georgiev, C. Guedes Soares, Sustainable Development and Innovations in Marine Technologies, 2019
M. Acanfora, T. Coppola, F. De Luca, D. Lauria
Over the last few decades, the application of Hilbert transform based method has excited a great deal of interest. This can motivated by the inherent ability of the Hilbert transform to formalize in a rigorous way the concept of instantaneous amplitude or phase of signals. At this purpose, the Hilbert transform is employed for defining the so called analytical signal: () z=x(t)+iH[x(t)]=x(t)+i1πP.V.∫−∞+∞x(τ)t−τdτ=A(t)eiφ(t)
Damage localization in beam-like structure under moving load by Empirical Mode Decomposition
Published in Jaap Bakker, Dan M. Frangopol, Klaas van Breugel, Life-Cycle of Engineering Systems, 2017
All methods for SHM and signal processing of structures have several advantages and disadvantages; their advantages sometimes exceed from their disadvantages and this issue leads to superiority of these methods. In spite of this, some of these methods are not sensitive to slight damages in a structure and they are not accurate sufficiently to determine them. Therefore, Huang (Huang et al, 1998) proposed a modern method for analyzing linear and non-linear responses to the structures called “The Hilbert–Huang transform (HHT)”. The method includes the empirical mode decomposition (EMD) and the Hilbert Transform (HT). Intrinsic mode functions (IMFs) of signals are calculated after decomposition of a signal using the EMD. These functions will have the modal specifications of a structure and usually any change of initial signal will be specified in first IMF. Moreover, the HT is also used on an IMF and instantaneous Hilbert frequency and instantaneous Hilbert amplitude values will be calculated. For instance, Yang et al (2003) employed the HHT method for an ASCE structure response. Frequency contents and damping ratio were calculated using the HHT method through phase curve and instantaneous amplitude. Razi & Taheri (2014) detected damage of marine pipes using laboratory and analytical models through energy damage detection index of the EMD. Chirp wave and impulsive force were used for excitation of pipe. Finally, the numerical and experimental results were compared. The results showed that the damage location in both modes had been detected with sufficient accuracy. Queck et al (2005) compared the results of wavelet analysis with the ones of the EMD method and showed that the EMD provides a direct method for extracting data for damage detection.
Some Further Analysis Methods
Published in Arthur W. Lees, Vibration Problems in Machines, 2020
A number of sections in the chapter discuss aspects of the analysis of nonstationary and nonlinear data, and no discussion of this topic would be complete without inclusion of the empirical mode decomposition, also known as the Huang–Hilbert transform. The technique was first published in 1998 (Huang et al., 1998) and has been applied widely for the analysis of nonstationary and nonlinear data. Data from machinery is, of course, often stationary being dominated by rotational speed effects, but there are instances in which full nonstationary analysis is an advantage.
Ballast fouling inspection and quantification with ground penetrating radar (GPR)
Published in International Journal of Rail Transportation, 2023
Yunlong Guo, Guixian Liu, Guoqing Jing, Jianjun Qu, Shilei Wang, Weile Qiang
Note that the same processing procedure was applied for the other indicators. The Hilbert transform of a continuous time signal x(t) is equal to the output response xh(t) after the signal has been passed through a linear system with impulse response h(t) = 1/πt. After the Hilbert transform, the amplitude of each frequency component in the frequency domain remains the same, but the phase will be shifted by 90°. This means that the signal is lagged by π/2 for positive frequencies and led by π/2 for negative frequencies; hence, the Hilbert transform is also known as a 90° phase shifter. The Hilbert transform is used to describe the envelope, instantaneous frequency and instantaneous phase of amplitude modulation or phase modulation, which makes the analysis easy and has important theoretical and practical value in communication systems. In communication theory, the Hilbert transform is a tool for analysing signals, and in digital signal processing, it can be used not only for signal transformation but also for filtering, and different types of Hilbert filters can be made. A more detailed explanation of the Hilbert transform can be found in [54].
Discharge and electromagnetic radiation behind the hole of simulated charging satellite surface under impact
Published in Waves in Random and Complex Media, 2022
Enling Tang, Liangliang Zhao, Yafei Han, Chuang Chen, Mengzhou Chang
Based on time–frequency analysis and processing of the time-domain signal, the change of frequency of electromagnetic field signals with time can be observed more intuitively. Assuming the non-linear and non-stationary characteristics of the experimental electromagnetic signals, the time–frequency analysis of the signals is carried out by using the Hilbert-Huang Transform (HHT). The core of the Hilbert-Huang transform is Empirical Mode Decomposition (EMD) and Hilbert transform (HT). EMD and HT are used to decompose and analyze signals, respectively. The purpose of the EMD algorithm is to decompose the signal with poor performance into a set of Intrinsic Mode Functions (IMF) with better performance. For the IMF, the following two properties must be satisfied: (1) The number of extreme points (maximum or minimum) and zero-crossing points of a signal is equal or the maximum difference is one; (2) The average value of the upper envelope consisting of local maxima and the lower envelope consisting of local minima is zero.
Identification, tracking and warning of vortex induced vibration using k-means clustering method
Published in Structure and Infrastructure Engineering, 2022
Min He, Peng Liang, Yang Zhang, Yang Wang, Kang-di Wang
An important problem for Hilbert transform that cannot be avoided is the end effect. The end effect happens when the signal to be transformed, which is a segment of signal extracted from the continuously monitored response, is not in integer period, and the transformed Hilbert signal would deviate from the true path in a limited range in the beginning and end of the signal, as shown in Figure 4, where the dotted line represents the wrong signal caused due to the end effect. The end effect would lead to an unreasonable SRA and therefore needs to be removed. Many methods have been proposed to eliminate the end effect, such as mirror periodic and extrema extending methods (Huang, Zhao, & Su, 2003; Rato, Ortigueira, & Batista, 2008). However, these methods are too complex and have restrictions to the original signals in application.