scholarly journals A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xianglei Liu ◽  
Mengzhuo Jiang ◽  
Ziqi Liu ◽  
Hui Wang
Keyword(s):  
Extreme Point ◽  
Filter Method ◽  
Signal Denoising ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Important Indicator ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Microwave Interferometry ◽  
Morphology Filter

Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

Extraction of Bridge Frequencies Inclusive of the Higher Modes by the ESMD Using the Contact-Point Response

2020 ◽  
Vol 20 (04) ◽  
pp. 2050045 ◽  
Author(s):  
Y. B. Yang ◽  
F. Xiong ◽  
Z. L. Wang ◽  
H. Xu
Keyword(s):  
Extreme Point ◽  
Systematic Study ◽  
Contact Point ◽  
Effective Procedure ◽  
Symmetric Mode ◽  
University Campus ◽  
Intrinsic Mode Functions ◽  
Higher Modes ◽  
Mode Decomposition ◽  
Better Than

An effective procedure is proposed for extracting bridge frequencies including the higher modes using the vehicle collected data. This is enabled by the use of the contact-point response, rather than the vehicle response, for processing by the extreme-point symmetric mode decomposition (ESMD). The intrinsic mode functions (IMFs) so decomposed are then processed by the FFT to yield the bridge frequencies. A systematic study is conducted to compare the proposed procedure with existing ones, while assessing the effects of various parameters involved. The proposed procedure was verified in the field for a two-span bridge located at the Chongqing University campus. It was confirmed to perform better than the existing ones in extracting bridge frequencies inclusive of the higher modes. The following are the reasons: (1) the ESMD is more efficient than the EMD in that remarkably less IMFs are generated; (2) the modal aliasing problem is largely alleviated, which helps enhancing the visibility of bridge frequencies in general; and (3) the contact-point response adopted is free of the vehicle frequency, which makes the higher frequencies more outstanding and detectable.

scholarly journals A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Guohui Li ◽  
Songling Zhang ◽  
Hong Yang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Extreme Point ◽  
Prediction Models ◽  
Original Data ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Model Based ◽  
Mode Decomposition ◽  
Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

scholarly journals Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1584 ◽  
Author(s):  
Xiaoying Tian ◽  
Yongshuai Li ◽  
Huan Zhou ◽  
Xiang Li ◽  
Lisha Chen ◽  
...  
Keyword(s):  
Extreme Point ◽  
Signal Denoising ◽  
Symmetric Mode ◽  
Nonlocal Means ◽  
Mode Decomposition ◽  
Electrocardiogram Signal

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

Quantifying the complexity of human colonic pressure signals using an entropy measure

2016 ◽  
Vol 61 (1) ◽  
pp. 127-132 ◽  
Author(s):  
Fei Xu ◽  
Guozheng Yan ◽  
Kai Zhao ◽  
Li Lu ◽  
Zhiwu Wang ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Colonic Motility ◽  
Approximate Entropy ◽  
Entropy Measure ◽  
Intrinsic Mode Functions ◽  
Spectral Entropy ◽  
Mode Decomposition ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Non Stationary Signal

Abstract Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

scholarly journals Time-Frequency Characteristics Analysis on Vibration Signals of a Three-Supported Rotor System with Misalignment

2011 ◽  
Vol 2-3 ◽  
pp. 717-721 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Mei Ling Wang ◽  
Li Jing Lin ◽  
Jian Wei Ji ◽  
Qing Kai Han
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Characteristics Analysis ◽  
Novel Method ◽  
Misalignment Fault

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

scholarly journals Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Peng-fei Zheng ◽  
Jun Wang
Keyword(s):  
Fault Diagnosis ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Coupling System ◽  
Reconstructed Signal ◽  
Gear Transmission System ◽  
Gear Fault ◽  
Diagnosis Method

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

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