scholarly journals Application of Boundary Local Feature Scale Adaptive Matching Extension EMD Endpoint Effect Suppression Method in Blasting Seismic Wave Signal Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Wu ◽  
Li Wu ◽  
Miao Sun ◽  
Ya-ni Lu ◽  
Yan-hua Han
Keyword(s):  
Seismic Signal ◽  
Local Feature ◽  
Characteristic Parameters ◽  
Suppression Effect ◽  
Mode Function ◽  
Mode Decomposition ◽  
Detailed Characteristic ◽  
Suppression Method ◽  
Change Trend ◽  
Feature Scale

The intrinsic endpoint effect of empirical mode decomposition (EMD) will lead to serious divergence of the intrinsic mode function (IMF) at the endpoint, which will lead to the distortion of IMF and affect the decomposition accuracy of EMD. In view of this phenomenon, an EMD endpoint effect suppression method based on boundary local feature scale adaptive matching extension was proposed. This method can consider both the change trend of the signal at the endpoint and the change rule of the signal inside. The simulation results showed that the proposed method had better suppression effect on the intrinsic endpoint effect of EMD than the traditional EMD endpoint effect suppression method and achieved high-precision IMF. The endpoint effect suppression method of EMD based on boundary local feature scale adaptive matching extension was used to process the actual blasting seismic signal. The decomposition results showed that the method can effectively suppress the endpoint effect of EMD of blasting seismic signal and are helpful to extract the detailed characteristic parameters of blasting seismic signal.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Study on Vibration Signal Marginal Spectrum of Paper-Transferring System in Printing Press Based on EMD

2010 ◽  
Vol 139-141 ◽  
pp. 2464-2468
Author(s):  
Yi Ming Wang ◽  
Shao Hua Zhang ◽  
Zhi Hong Zhang ◽  
Jing Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Design ◽  
Impact Load ◽  
Vibration Signal ◽  
Key Factors ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Periodic Signals ◽  
The Moment

The precision of transferring paper is key factors to decide the print overprint accuracy, and vibration has an important impact on paper transferring accuracy. Empirical mode decomposition (EMD) can be used to extract the features of vibration test signal. According to the intrinsic mode function (IMF) by extracted, it is useful to analyze the dynamic characteristics of swing gripper arm on motion state. Due to the actual conditions of printing, the vibration signal of Paper-Transferring mechanism system is complex quasi periodic signals. Hilbert-Huang marginal spectrum that is based on empirical mode decomposition can solve the problem which is modals leakage by FFT calculated in frequency domain. Through the experimental research, the phase information of impact load at the moment of grippers opening or closing, which can be used for the optimization design of Paper-Transferring system and the improvement in the accuracy of swing gripper arm.

scholarly journals Time-Frequency Analysis and Application of a Vibration Signal of Tunnel Excavation Blasting Based on CEEMD-MPE-HT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Band ◽  
Frequency Analysis ◽  
Frequency Characteristic ◽  
Permutation Entropy ◽  
Natural Vibration Frequency ◽  
Characteristic Parameters ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.

scholarly journals Determination of variation uncertainty in runoff time series at multi-temporal scales

2021 ◽  
Author(s):  
Yan Ye ◽  
Jinping Zhang ◽  
Xunjian Long ◽  
Lihua Ma ◽  
Yong Ye
Keyword(s):  
Time Series ◽  
Annual Runoff ◽  
Important Variable ◽  
Temporal Scales ◽  
Flood Season ◽  
Mode Function ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Emd Method

Abstract In order to survey the possible periodic, uncertainty and common features in runoff with multi-temporal scales, the empirical mode decomposition (EMD) method combined with the set pair analysis (SPA) method was applied, with data observed at Zhangjiashan hydrological station. The results showed that the flood season and annual runoff time series consisted of four intrinsic mode function (IMF) components, and the non-flood season time series exhibited three IMF components. Moreover, based on the different coupled set pairs from the time series, the identity, discrepancy, and contrary of different periods at multi-temporal scales were determined by the SPA method. The degree of connection μ between the flood season and annual runoff periods were the highest, with 0.94, 0.77, 0.7 and 0.73, respectively, and the μ between the flood periods and the non-flood periods were the lowest, with 0.66, 0.46, 0.24 and 0.24, respectively. Third, the maximum μ of each SPA appeared in the first mode function. In general, the different extractive periods decomposed by EMD method can reflected the average state of Jinghe River. Results also verified that runoff suffered from seasonal and periodic fluctuations, and fluctuations in the short-term corresponded to the most important variable. Therefore, the conclusions draw in this study can improve water resources regulation and planning.

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application

2021 ◽  
Vol 379 (2192) ◽  
pp. 20200239 ◽  
Author(s):  
Chen Yang ◽  
Jianhua Yang ◽  
Dengji Zhou ◽  
Shuai Zhang ◽  
Grzegorz Litak
Keyword(s):  
Stochastic Resonance ◽  
Bistable System ◽  
Nonlinear Frequency ◽  
Bearing Fault Diagnosis ◽  
Mode Function ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Fourier Transform Spectrum ◽  
Short Time ◽  
Sr Method

The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

scholarly journals Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jia ◽  
Fuqi Ma ◽  
Jian Dang ◽  
Guangyi Liu ◽  
Huizhi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Empirical Mode Decomposition ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Large Wind

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

Over-Sifting Components Analysis in Bidimensional Empirical Mode Decomposition

2010 ◽  
Vol 159 ◽  
pp. 377-382
Author(s):  
Guang Tao Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Mean ◽  
Components Analysis ◽  
Bidimensional Empirical Mode Decomposition

Define the course of getting mean envelope as an operation (mean envelope operation) in Empirical mode decomposition (EMD), so as to express the Intrinsic Mode Function (IMF) with mean envelopes. Summarize several rules of the mean envelope operation. On this fundamental, the abnormal components exist in the over-sifting IMFs are extracted out, and the conclusion is testified with the infinite sifting experiment.

scholarly journals The seismic signatures of the 2009 Shiaolin landslide in Taiwan

2011 ◽  
Vol 11 (5) ◽  
pp. 1559-1569 ◽  
Author(s):  
Z. Feng
Keyword(s):  
Surface Wave ◽  
Seismic Waves ◽  
Frequency Spectra ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
Mode Function ◽  
Mode Decomposition ◽  
Weak Surface

Abstract. The Shiaolin landslide occurred on 9 August 2009 after Typhoon Morakot struck Taiwan, claiming over 400 lives. The seismic signals produced by the landslide were recorded by broadband seismic stations in Taiwan. The time-frequency spectra for these signals were obtained by the Hilbert-Huang transform (HHT) and were analyzed to obtain the seismic characteristics of the landslide. Empirical mode decomposition (EMD) was applied to differentiate weak surface-wave signals from noise and to estimate the surface-wave velocities in the region. The surface-wave velocities were estimated using the fifth intrinsic mode function (IMF 5) obtained from the EMD. The spectra of the earthquake data were compared. The main frequency content of the seismic waves caused by the Shiaolin landslide were in the range of 0.5 to 1.5 Hz. This frequency range is smaller than the frequency ranges of other earthquakes. The spectral analysis of surface waves (SASW) method is suggested for characterizing the shear-wave velocities of the strata in the region.

scholarly journals Bolt Detection Signal Analysis Method Based on ICEEMD

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhui Guo ◽  
Zhan Zhang ◽  
Xin Xie ◽  
Zhengyu Yang
Keyword(s):  
Signal Analysis ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Analysis Method ◽  
Détection Signal ◽  
Mode Function ◽  
Mode Decomposition ◽  
Signal Analysis Method ◽  
Detection Signal

The construction quality of the bolt is directly related to the safety of the project, and, as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold denoising technique. Based on the approximate entropy and the wavelet denoising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions and also that the IMF can effectively identify the reflection signal of the end of the bolt.

Sign in / Sign up

Export Citation Format

Share Document