Seismic Wave Propagation Characteristics and Their Effects on the Dynamic Response of Layered Rock Sites

To investigate the seismic response of layered rock sites, a multidomain analysis method was proposed. Three finite element models with infinite element boundaries for layered sites were analysed. The results of this multidomain analysis show that stratum properties and elevation have an impact on wave propagation characteristics and the dynamic response of layered sites. Compared with the rock mass, the overlying gravel soil has a greater dynamic amplification effect at the sites. A time domain analysis parameter PGA(IMF) was proposed to analyse the effects of different strata on the seismic magnification effect of layered sites, and its application was also discussed in comparison with PGA. According to the frequency domain analysis, the interface of the rock mass strata has a low impact on the Fourier spectrum characteristics of the sites, but gravel soil has a great magnification effect on the spectrum amplitude in the high-frequency band (≥30 Hz) of waves. Moreover, the stratum properties have a great influence on the shape and peak value of the Hilbert energy and marginal spectrum at layered sites. When waves propagate from hard rock to soft rock, the peak value of the Hilbert energy spectrum changes from single to multiple peaks; then, in gravelly soil, the Hilbert energy spectral peak, its nearby amplitude and the amplitude in the high-frequency band (28–36 Hz) are obviously amplified. The frequency components and amplitude of the marginal spectrum become more abundant and larger from rock to gravelly soil in the high-frequency band (28–35 Hz).

Research on stator current characteristics of centrifugal pumps under different mechanical seal failures

Mechanical seal failure has a great negative impact on the operation of a centrifugal pump system. A method to analyze the stator current characteristics of the motor in a centrifugal pump system is proposed to monitor the internal flow of the centrifugal pump and to identify the failure status of the mechanical seal. Experiments were conducted under different mechanical seal states. Based on sensorless technology, the stator current signal of the motor is collected, processed by windowing function, anti-aliasing filter, singular value decomposition, Hilbert–Huang transform, and the marginal spectrum of correlation quantity is drawn. The results show that according to the external characteristic curve of the centrifugal pump, after the failure of the mechanical seal, the head and efficiency of the centrifugal pump decrease, and the head is greatly affected by the degree of failure, while the degree of mechanical seal failure has little effect on the shaft power of the centrifugal pump; the centrifugal pump has good operation stability under design conditions or near slightly large flow; the stability of centrifugal pump operation decreases with the aggravation of mechanical seal failure; the corresponding maximum amplitude in the marginal spectrum can be used as an index to diagnose the damage degree of the mechanical seal.

Vibration Signal Analysis of Chimney Blasting Based on HHT

In the blasting demolition processs of high-rise structures, the impact of blasting vibration to the environment and objects to be protected must be effectively controlled, so the blasting vibration signal is deeply analyzed [1]. In this paper, the blasting vibration signal of a chimney is analyzedbased on HHT. The blasting vibration signal is denoised by Empirical Mode Decomposition (EMD)-wavelet threshold, then using Hilbert-Huang Transform (HHT) [2] the measured blasting vibration waveform Hilbert spectrum, marginal spectrum and instantaneous energy graph are draw to analyze the chimney blasting vibration. The results show that the denoising effect of EMD-wavelet threshold is good for blasting vibration signal [3]. HHT method has a good feature identification ability when processing vibration signals, and can reflect the characteristics of data more comprehensively and accurately, which provides convenience for the study of vibration signal data.

Fluctuating Characteristics of the Stilling Basin with a Negative Step Based on Hilbert-Huang Transform

The violent fluctuation of hydrodynamic pressure in stilling basins is an important factor threatening the safety of the bottom plates of stilling basins, and plays an important role in the safe operation of stilling basins. In order to deeply understand the fluctuating characteristics of stilling basins, the fluctuating pressure signal of a stilling basin bottom plate is processed by the Hilbert-Huang transform method through a hydraulic model test. In this paper, three signal decomposition methods are used to decompose the pulsating pressure signal. A Hilbert transform is used to select the component with the best decomposition effect. The time-frequency-amplitude diagram of the pulsating pressure signal is obtained by Hilbert transform, and its time-frequency characteristics are discussed in depth. The analysis results are as follows: (a) the decomposition results from the CEEMD method are orthogonal and complete. The HHT method is suitable for processing fluctuating pressure signals. (b) With an increase in IMF decomposition order, the signal frequency band becomes narrow, the Hilbert spectrum amplitude decreases and the pulsating pressure energy decreases. The decomposition of the fluctuating pressure signal into components of different scales shows that the turbulence is composed of multiple scales of vortices, reflecting the vortex structure in the turbulence. (c) The jet impingement zone of the drop bucket stilling basin is near x/L = 0.075. The dominant frequency and marginal spectrum energy of the jet impingement zone are very prominent, and the marginal spectrum energy is mostly concentrated within 5.0 Hz. (d) At different drop height and different flow energy ratio, the fluctuation in the dominant frequency of fluctuating pressure decreases, the dominant frequency of the head of the stilling basin is larger, the dominant frequency of the middle and rear parts tends to be stable, and the dominant frequency is finally stabilized at about 1.0 Hz. This paper attempts to use the HHT method to process the fluctuating pressure signal, and the results provide a new discussion method for exploring the fluctuating pressure characteristics of hydraulic structures.

Time-Frequency Characteristics of Fluctuating Pressure on the Bottom of the Stilling Basin with Step-Down Floor Based on Hilbert–Huang Transform

Fluctuating pressure is an important feature of the bottom of a stilling basin with step-down floor. To analyze the frequency domain characteristics and energy distribution of this fluctuating pressure, the Hilbert–Huang transform (HHT) method is used. First, empirical mode decomposition is performed on the pressure fluctuation signal to obtain a number of intrinsic mode functions (IMFs), and then the Hilbert transformation is performed on each IMF to obtain the Hilbert spectrum and marginal spectrum for characterizing the pressure fluctuation signal. The results show that the fluctuating pressure signal of the stilling basin with step-down floor has obvious characteristics of low frequency and large amplitude. The dominant frequencies of the head and tail of the stilling basin are very prominent, and most of the energy is concentrated below 5.0 Hz; with the increase in the relative position of the measuring point, the energy distribution in stilling basin with step-down floor changes from high-frequency component to low-frequency component. The fluctuating pressure signal of the stilling basin with step-down floor has random amplitude modulation and frequency modulation. The marginal spectrum obtained by the HHT method can obtain the local characteristics of the signal more accurately and is more suitable for processing nonlinear and nonstationary signals.

A Novel Feature Extraction Method for the Condition Monitoring of Bearings

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

Analysis method of the cavitation vibration signals in poppet valve based on EEMD

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

Time-frequency energy analysis of deepwater explosion shock wave signals based on HHT

In order to study the time-frequency characteristics of shock wave signals under deep water explosion conditions, experiments are performed using water medium explosion containers to simulate different water depth conditions, and signal analysis is performed on the shock wave data obtained in the experiments. Traditional time-frequency analysis methods such as Fourier transform and wavelet transform have many limitations on deep-water explosion shock wave signal analysis, the HHT method is used to analyse the experimental data from the three-dimensional Hilbert spectrum, marginal spectrum and instantaneous energy spectrum. The results show that the time-frequency method can effectively extract the frequency components of the deep-water explosion load signal in different periods. It provides a reference for people to understand the time frequency characteristics of shock wave signals in deep water.