Signal processing for the guided wave test based on the empirical mode decomposition

2011 ◽  
Author(s):  
Jiang Xu ◽  
Hongfen Xiong ◽  
Xinjun Wu
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Guided Wave ◽  
Mode Decomposition

Signal Processing of Motor Imagery EEG Waves Using Empirical Mode Decomposition

2017 ◽  
pp. 197-209
Author(s):  
Ajithkumar Sreekumar ◽  
M. Uttara Kumari ◽  
Krishna Chaithanya Vastare ◽  
Suraj Madenur Sreenivasa ◽  
N. Apoorva
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Mode Decomposition

Damage detection of structures using signal processing and artificial neural networks

2019 ◽  
Vol 23 (5) ◽  
pp. 884-897 ◽  
Author(s):  
Seyed Bahram Beheshti Aval ◽  
Vahid Ahmadian ◽  
Mohammad Maldar ◽  
Ehsan Darvishan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signal Processing ◽  
Damage Detection ◽  
Empirical Mode Decomposition ◽  
Degree Of Freedom ◽  
Benchmark Problem ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Artificial Neural

This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.

The Combination of Empirical Mode Decomposition and Minimum Entropy Deconvolution for Roller Bearing Diagnostics in Non-Stationary Operation

2012 ◽  
Author(s):  
R. Ricci ◽  
P. Borghesani ◽  
S. Chatterton ◽  
P. Pennacchi
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Minimum Entropy ◽  
Bearing Diagnostics ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
The Hilbert Transform ◽  
Processing Techniques

Diagnostics is based on the characterization of mechanical system condition and allows early detection of a possible fault. Signal processing is an approach widely used in diagnostics, since it allows directly characterizing the state of the system. Several types of advanced signal processing techniques have been proposed in the last decades and added to more conventional ones. Seldom, these techniques are able to consider non-stationary operations. Diagnostics of roller bearings is not an exception of this framework. In this paper, a new vibration signal processing tool, able to perform roller bearing diagnostics in whatever working condition and noise level, is developed on the basis of two data-adaptive techniques as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED), coupled by means of the mathematics related to the Hilbert transform. The effectiveness of the new signal processing tool is proven by means of experimental data measured in a test-rig that employs high power industrial size components.

scholarly journals An Improved Empirical Mode Decomposition Method for Vibration Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaohan Liu ◽  
Guangfeng Shi ◽  
Weina Liu
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

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