An empirical mode decomposition-based frequency-domain approach for the fatigue analysis of nonstationary processes

2018 ◽  
Vol 41 (9) ◽  
pp. 1980-1996 ◽  
Author(s):  
Qian Niu ◽  
Shi-xi Yang ◽  
Xing-lin Li
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Fatigue Analysis ◽  
Nonstationary Processes ◽  
Mode Decomposition ◽  
Frequency Domain Approach

SMOOTHING EMPIRICAL MODE DECOMPOSITION: A PATCH TO IMPROVE THE DECOMPOSED ACCURACY

2010 ◽  
Vol 02 (04) ◽  
pp. 521-543 ◽  
Author(s):  
SUN-HUA PAO ◽  
CHIEH-NENG YOUNG ◽  
CHIEN-LUN TSENG ◽  
NORDEN E. HUANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Cubic Spline ◽  
Nonstationary Processes ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Functional Forms ◽  
Nonstationary Data ◽  
Nonlinear Decomposition ◽  
Cubic Spline Function ◽  
Smoothing Procedure

Hilbert-Huang Transformation (HHT) is designed especially for analyzing data from nonlinear and nonstationary processes. It consists of the Empirical Mode Decomposition (EMD) to generate Intrinsic Mode Function (IMF) components, from which the instantaneous frequency can be computed for the time-frequency Hilbert spectral Analysis. Currently, EMD, based on the cubic spline, is the most efficient and popular algorithm to implement HHT. However, EMD as implemented now suffers from dependence on the cubic spline function chosen as the basis. Furthermore, due to the various stoppage criteria, it is difficult to establish the uniqueness of the decomposition. Consequently, the interpretation of the EMD result is subject to certain degree of ambiguity. As the IMF components from the classic EMD are all approximations from the combinations of piece-wise cubic spline functions, there could also be artificial frequency modulation in addition to amplitude modulation. A novel Smoothing Empirical Mode Decomposition (SEMD) is proposed. Although SEMD is also an approximation, extensive tests on nonlinear and nonstationary data indicate that the smoothing procedure is a robust and accurate approach to eliminate the dependence of chosen spline functional forms. Thus, we have proved the uniqueness of the decomposition under the weak limitation of spline fittings. The natural signal length-of-day 1965–1985 was tested for the performance in nonstationary and nonlinear decomposition. The resulting spectrum by SEMD is quite stable and quantitatively similar to the optimization of EMD.

Identification of tooth fault in a gearbox based on cyclostationarity and empirical mode decomposition

2017 ◽  
Vol 17 (3) ◽  
pp. 494-513 ◽  
Author(s):  
Jong-Sik Kim ◽  
Sang-Kwon Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
The Impact

In the previous work, the cyclostationarity process, which is one of signal processing methods, has been used in health monitoring of the rotating machinery because of the superior detecting property of hidden periodicity. However, it is often difficult to acquire the information about the hidden periodicity due to the fault of the rotating machinery when the impact signal is low. Therefore, a certain preprocessing tool to extract the information about the impact signal due to the fault is required. This article presents the new detection process of tooth faults in a gearbox system based on the empirical mode decomposition algorithm which adaptively decomposes the signal into a set of intrinsic mode functions and the cyclostationarity process which identifies the hidden periodicity clearly in bi-frequency domain. The proposed method was demonstrated with a simulated signal and was applied to the detection of four types of conditions of tooth fault successfully.

scholarly journals Collaborative Sleep Electroencephalogram Data Analysis Based on Improved Empirical Mode Decomposition and Clustering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiangwei Zheng ◽  
Xiaochun Yin ◽  
Xuexiao Shao ◽  
Yalin Li ◽  
Xiaomei Yu
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Clustering Algorithm ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stage Classification ◽  
Sleep Stage Classification

Sleep-related diseases seriously affect the life quality of patients. Sleep stage classification (or sleep staging), which studies the human sleep process and classifies the sleep stages, is an important reference to the diagnosis and study of sleep disorders. Many scholars have conducted a series of sleep staging studies, but the correlation between different sleep stages and the accuracy of classification still needs to be improved. Therefore, this paper proposes an automatic sleep stage classification based on EEG. By constructing an improved empirical mode decomposition and K-means experimental model, the concept of “frequency-domain correlation coefficient” is defined. In the process of feature extraction, the feature vector with the best correlation in the time-frequency domain is selected. Extraction and classification of EEG features are realized based on the K-means clustering algorithm. Experimental results demonstrate that the classification accuracy is significantly improved, and our proposed algorithm has a positive impact on sleep staging compared with other algorithms.

Denoising signals for photoacoustic imaging in frequency domain based on empirical mode decomposition

Optik ◽  
2018 ◽  
Vol 160 ◽  
pp. 402-414 ◽  
Author(s):  
Qinglin Kong ◽  
Qian Song ◽  
Yan Hai ◽  
Rui Gong ◽  
Jietao Liu ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Photoacoustic Imaging ◽  
Mode Decomposition

An Application of the Frequency-Domain Experience Mode Decomposition to Enhance Deconvolution Results

2013 ◽  
Vol 397-400 ◽  
pp. 2120-2123
Author(s):  
Ya Nan Zhang ◽  
Yong Shou Dai ◽  
Jin Jie Ding ◽  
Man Man Zhang ◽  
Rong Rong Wang
Keyword(s):  
Reflection Coefficient ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Amplitude Spectrum ◽  
Seismic Section ◽  
Mode Decomposition ◽  
The Time Domain ◽  
Non Stationary Signal ◽  
Different Characteristics ◽  
Emd Method

To improve the resolution of the seismic section after deconvolution, a method based on frequency-domain experience mode decomposition was proposed. Empirical mode decomposition (EMD) method is usually used to analyze the time domain non-stationary signal, in order to better recover original reflection coefficient sequence, empirical mode decomposition was implemented for frequency-domain amplitude spectrum. Through the different characteristics between the equivalent filter amplitude after deconvolution and reflection coefficient sequence amplitude in frequency-domain, the real reflection coefficient sequence was recovered. Simulation results indicate that the method is effective and feasible.

Frequency Domain Fatigue Analysis for a Unbonded Flexible Riser: Damage Induced by Dynamic Bending

2020 ◽  
Author(s):  
Jiabei Yuan ◽  
Yucheng Hou ◽  
Zhimin Tan
Keyword(s):  
Tensile Stress ◽  
Frequency Domain ◽  
Fatigue Damage ◽  
Time Domain ◽  
Time History ◽  
Domain Analysis ◽  
Fatigue Analysis ◽  
Stress Spectrum ◽  
History Of ◽  
Frequency Domain Approach

Abstract The service life of flexible risers is a vital design parameter in offshore field development. The standard approach to calculate fatigue life is the nonlinear time-domain analysis. The approach uses time history of riser responses in local structure assessment to get the fatigue damage of tensile layers. Another approach is the linearized frequency-domain analysis. Instead of using time history of stress and rainflow counting technique, the approach uses stress spectrum and empirical mathematical terms to estimate the fatigue damage. The frequency domain approach is significantly faster. However, due to the whole system being linearized, the latter usually produces different results and is considered to be less accurate than the time domain approach. To address this issue, Baker Hughes previously developed an approach which uses the frequency domain technique as base solution and calibration factors from limited time domain cases. The approach is limited to tensile wires at the end fitting entrance where tension and tensile stress is directly linked. In this paper, a similar approach is proposed to be applied for tensile fatigue at all regions, whose tensile stress are induced by a combination of tension, pressure, bending and friction between layers. Since tensile stress is not directly related to any single riser response, the stress spectrum is predicted by using a transfer function. With the predicted stress spectrum, the fatigue damage of each case is calculated with Dirlik’s method and SN curves. The paper summarizes the development of the hybrid frequency domain approach. The fatigue damage of risers from several projects are acquired with both time domain and frequency domain approaches. The approach is significantly faster than traditional time domain approach and produces conservative results. Furthermore, discussions are made on options to improve the approach and reduce the conservatism in the frequency domain fatigue analysis.

scholarly journals ANALYSIS OF THE PRECIPITATION CLIMATE SIGNAL USING EMPIRICAL MODE DECOMPOSITION (EMD) OVER THE CASPIAN CATCHMENT AREA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 923-929
Author(s):  
F. Sabzehee ◽  
V. Nafisi ◽  
S. Iran Pour ◽  
B. D. Vishwakarma
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Precipitation Time Series ◽  
Climate Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Instantaneous Frequencies ◽  
Better Than

Abstract. In this paper, we employ Empirical Mode Decomposition (EMD) together with Hilbert Transform to analyze precipitation time series over the Caspian Sea catchment. Several studies have shown that EMD can extract nonlinear and non-stationary signals better than Fast Fourier Transform (FFT) and Wavelet Transform. EMD decomposes the time series into a finite number of Intrinsic Mode Functions (IMFs) in the time-frequency domain, while FFT helps us operate either in the time or the frequency domain, which fuels limitations such as the inability of nonstationary signal processing and the lack of time transparency. Although Wavelet Transform is shown to be better than FFT, it fails to detect the instantaneous frequencies and needs to have prior information about characteristics of the data. On the other hand, EMD has shown that it is almost able to determine the signal characteristics with no previous assumptions to estimate the instantaneous frequencies of the signal. In this work, EMD is applied to identify the main frequencies of precipitation time series. Thereafter, a statistical procedure is used to identify the prominent IMF of the original signal.We use the correlation coefficient, Minkowski distance and variance test to extract the relevant and prominent IMFs. The results show that IMF 1–3 are the relevant components and are related to annual and biennial variations of precipitation time series over the Caspian catchment during 2003–2016, respectively.

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