Pipeline Leak Detection Based on Acoustic Emission Using Empirical Mode Decomposition Method

2008 ◽  
Author(s):  
Yibo Li ◽  
Junlin Li ◽  
Liying Sun ◽  
Shijiu Jin ◽  
Shenghua Han
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Signal Analysis ◽  
Leak Detection ◽  
Detection Sensitivity ◽  
Intrinsic Mode Functions ◽  
Location Accuracy ◽  
Noise Interference ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

Corrosion in pipeline is a significant problem in the oil industry and there is also much interest in reducing leak due to corrosion. Correlation techniques are widely used in leak detection, and these have been extremely effective when attempting to locate leaks in metal pipes. Acoustic emission is a new non-destructive pipeline inspection technology which can be used to monitor crucial part of pipelines and detect pipe corrosion or leak in real time. However, AE signals causing by corrosion and leak are liable to noise interference on field. Aiming at solving the noise interference problems and increase the detection sensitivity and location accuracy of the leak, advanced signal analysis method based on Empirical Mode Decomposition were researched. Empirical Mode Decomposition is a great breakthrough in non-stable signal analysis and it decomposes the signals into a sum of finite intrinsic mode functions (IMF), which have real physical meaning. In the experiment, the leak signals from a 30 m pipeline were decomposed into 9 intrinsic mode functions by EMD, among which some IMF components containing typical AE characteristic can be selected to reconstruct the signal, and thus intrinsic characteristic of leak signal could be extracted and noise interference would be eliminated. Location accuracy of the leaking hole calculated with the reconstructed signals based on EMD algorithm was increased 64%.

Application of the Empirical Mode Decomposition Method to the Identification of Disc Brake Squeal

2002 ◽  
Author(s):  
Fulun Yang ◽  
Chin An Tan ◽  
Frank Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Disc Brake ◽  
Intrinsic Mode Functions ◽  
Brake Squeal ◽  
Mode Decomposition ◽  
Out Of Plane ◽  
Empirical Mode Decomposition Method ◽  
Original Application ◽  
The Hilbert Transform

This paper investigates the identification of mechanisms of disc brake squeal by the application of a recently developed Empirical Mode Decomposition method (EMD). A known strength of the EMD is its adaptive nature in analyzing nonstationary data, with success in its original application to ocean mechanics. The EMD decomposes an original signal into a number of intrinsic mode functions (IMFs), with each IMF often containing distinct physical significance. Several sets of disc brake squeal data were obtained and processed by EMD. A typical set data is presented in this paper for discussion. Employing a sifting process in the EMD, four prominent squeal-related IMFs are identified in this set of data. The Hilbert transform is then used to analyze the frequency and amplitude contents of the four IMFs, and it is shown that the first IMF is dominant. The spectrogram method is applied to analyze the time-evolution of the frequency components of the IMFs in the squeal process. This analysis procedure confirms an important squeal mechanism, i.e., the squeal condition is governed by the coupling of in-plane and out-of-plane vibration modes of the rotor and the coalescence of their natural frequencies. The inverse approach outlined in this paper is shown to be useful for providing new insights and confirming established hypotheses of disc brake squeal.

scholarly journals A VARIANT OF THE EMD METHOD FOR MULTI-SCALE DATA

2009 ◽  
Vol 01 (04) ◽  
pp. 483-516 ◽  
Author(s):  
THOMAS Y. HOU ◽  
MIKE P. YAN ◽  
ZHAOHUA WU
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Separation Property ◽  
Intrinsic Mode Functions ◽  
Scale Separation ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Emd Method ◽  
Scale Data

In this paper, we propose a variant of the Empirical Mode Decomposition method to decompose multiscale data into their intrinsic mode functions. Under the assumption that the multiscale data satisfy certain scale separation property, we show that the proposed method can extract the intrinsic mode functions accurately and uniquely.

scholarly journals Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

2021 ◽  
Vol 12 ◽  
pp. 7
Author(s):  
Junbing Shi ◽  
Yingmin Wang ◽  
Xiaoyong Zhang ◽  
Libo Yang
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Threshold Method ◽  
Underwater Acoustic ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Combined Algorithm

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

Feature Extraction of the Crack AE Signal Using HHT Transform

2013 ◽  
Vol 340 ◽  
pp. 441-444
Author(s):  
K.F. He ◽  
Z.J. Zhang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Crack Depth ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Ae Signal

The use of Hilbert-Huang transform (Hilbert-Huang transform, HHT) on crack AE signal study, through empirical mode decomposition (empirical mode decomposition, EMD) AE signal is decompose into a number of intrinsic mode functions (Intrinsic mode Function, IMF), Hilbert spectrum and Hilbert marginal spectrum are calculated. The results show that crack depth structure bearing of acoustic emission are detected accurately by the number of acoustic emission events, time and crack the degree from Hilbert spectrum and Hilbert marginal spectrum.

scholarly journals Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

2020 ◽  
Vol 12 (3) ◽  
pp. 168781402091053
Author(s):  
Yanfeng Peng ◽  
Junhang Chen ◽  
Ruiqiong Luo ◽  
Xiaojun Xie ◽  
Xianyu Zhu ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Problem ◽  
Narrow Band ◽  
Ensemble Empirical Mode Decomposition ◽  
Reconstruction Error ◽  
Intrinsic Mode Functions ◽  
Noise Interference ◽  
Mode Decomposition ◽  
Adaptive Decomposition ◽  
Gear Crack

Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.

Engineering Surface Analysis by Bidimensional Empirical Mode Decomposition

2013 ◽  
Vol 694-697 ◽  
pp. 2823-2828 ◽  
Author(s):  
Zheng Kai Zhang ◽  
Li Chen Gu ◽  
Yong Sheng Zhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Friction And Wear ◽  
Intrinsic Mode Functions ◽  
Wear Properties ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Friction And Wear Properties ◽  
Bidimensional Empirical Mode Decomposition

It is well known that an engineering surface is composed of a large number of wavelengths of roughness that are superimposed on each other. Because these multi-scale features are related to different aspects of the processes the surface has undergone and closely related to the friction and wear properties of a surface, the analysis and characterization of these features becomes an important aspect of manufacture. The challenge is how to use them for acquiring knowledge and for aid to analysis. In this paper, a method for surface topography analysis is proposed based on bidimensional empirical mode decomposition (BEMD), which can provide good adaptive separation of surface texture into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). Applications are conducted by using a simulated surfaces to demonstrate the feasibility and applicability of using the bidimensional empirical mode decomposition method in the analysis of engineering surfaces.

scholarly journals Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages

2019 ◽  
Vol 9 (13) ◽  
pp. 2587 ◽  
Author(s):  
Chun-Yao Lee ◽  
Kuan-Yu Huang ◽  
Yu-Hua Hsieh ◽  
Po-Hung Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Electrical Discharge Machining ◽  
Detection System ◽  
Detection Accuracy ◽  
Intrinsic Mode Functions ◽  
Noise Interference ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Lower Complexity ◽  
Motor Bearing

This paper proposes a model which uses the greedy algorithm to select the optimal intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD), namely the greedy empirical mode decomposition (GEMD) model. The optimal IMFs can more sufficiently represent the characteristics of damage bearings since the proposed GEMD model effectively selects some IMFs not affected by noise. To validate the superiority of the proposed GEMD model, various damage types of motor bearings were shaped by electrical discharge machining (EDM) in this experiment. The measured motor current signals of various types were decomposed to IMFs by using EMD. Then the optimal IMFs can be obtained by using the proposed GEMD model. The results show that the Hilbert–Huang transform (HHT) spectrums when using the optimal IMFs become easier in the detection system than when using all IMFs. Simultaneously, the detection accuracy of motor bearing damages is increased by using the features extracted from the lower complexity HHT spectrum. The average detection accuracy can be also improved from 69.5% to 74.6% by using the features extracted from the GEMD-HHT spectrum even in a noise interference 10dB

scholarly journals The extraction of informative vibration components of bearings by different adaptive methods

2021 ◽  
Author(s):  
Serhii Mykhalkiv
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Adaptive Noise ◽  
Impulse Modulation

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

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