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.

scholarly journals Corrigendum to “Bolt Detection Signal Analysis Method Based on ICEEMD”

2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Chunhui Guo ◽  
Zhan Zhang ◽  
Xie Xin ◽  
Zhengyu Yang
Keyword(s):  
Signal Analysis ◽  
Analysis Method ◽  
Détection Signal ◽  
Signal Analysis Method ◽  
Detection Signal

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

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

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.

Research on Applied-Information Technology in Digital Speech Based on LMD Algorithm

2014 ◽  
Vol 1014 ◽  
pp. 447-451
Author(s):  
Dong Kang He ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Production Function ◽  
Signal Analysis ◽  
Training Model ◽  
Recognition System ◽  
Analysis Method ◽  
End Effects ◽  
Local Mean ◽  
Non Stationary Signal ◽  
Signal Analysis Method

As a new nonlinear and non-stationary signal analysis method,local mean decomposition (LMD) has a good adaptability. We decompose the original non-stationary acceleration vibration signals into several stationary production function (PF).But performing LMD will produce end effects which make results distorted. A hidden Markov model (HMM)-based speech recognition system for Chinese spell.After analyzing reasons for end effects of LMD in detail,a new method based on weighted matching similar waveform was proposed.Experiments in speech recognition to the production function as the training model, the more traditional identification method to identify higher rates. LMD is an effective method. It is feasible to extract the feature from speech signals with LMD.

Adaptive denoising for laser detection signal of shell thickness based on variational mode decomposition

2017 ◽  
Vol 25 (8) ◽  
pp. 2173-2181
Author(s):  
李加福 LI Jia-fu ◽  
唐文彦 TANG Wen-yan ◽  
张晓琳 ZHANG Xiao-lin ◽  
王 军 WANG Jun
Keyword(s):  
Shell Thickness ◽  
Variational Mode Decomposition ◽  
Détection Signal ◽  
Laser Detection ◽  
Mode Decomposition ◽  
Detection Signal ◽  
Adaptive Denoising

scholarly journals Ultrasonic Testing Analysis of Concrete Structure Based on S Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Juncai Xu ◽  
Hai Wei
Keyword(s):  
Concrete Structure ◽  
Ultrasonic Testing ◽  
Analysis Method ◽  
Détection Signal ◽  
Data Set ◽  
Time Frequency ◽  
S Transform ◽  
Spectral Analysis Method ◽  
Time Invariant ◽  
Detection Signal

Ultrasonic testing analysis is a crucial scientific component during the process and interpretation of the ultrasonic detection signal. Focusing on the ultrasonic testing characteristics, the time-invariant spectral analysis method cannot meet the processing requirements of the detection signal fully. Thus, S transform, the time-varying analysis method, was introduced into the ultrasonic testing data processing of the concrete structure. The acoustic wave phase velocity was derived based on the spectrum analysis, and the S transform time-frequency analysis method was established. Finally, based on the method of concrete experimental data set, studies show that the frequency energy spectrum with S transform can realize flexible and effective identification of defects in the concrete structure. Definitely, this analysis method can significantly improve the resolution and practicality of ultrasonic testing.

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