Mode Decomposition and Simulation of Strong Ground Motion Distribution using Singular Value Decomposition

We present a comparison of microseismic data denoising methods based on their effect on the polarization attributes of 3C microseismic signals. The compared denoising methods include the classical band-pass filtering, and three recently proposed denoising techniques: restricted domain hyperbolic Radon transform denoising, singular value decomposition-based reduced-rank filtering, and empirical mode decomposition denoising. In order to draw the comparison, we have denoised 3C synthetic data contaminated with noise extracted from actual field data records, calculated their rectilinearity, azimuth, and dip polarization attributes, and arranged them into histograms. The comparison has been drawn by measuring the distances between the polarization histograms of the clean and denoised data, assuming that one method outperforms another if the aforementioned distance is smaller. This strategy allows to quantify the improvement in the calculated polarization attributes due to the different denoising processes. In addition, we have also calculated the quality factor of the denoised signals, which adds value and robustness to the comparison. Our results have indicated that the method based on singular value decomposition preserves the original polarization attributes better than the other techniques tested in this work. Moreover, it has also retrieved the denoised signal with the highest quality factor. Finally, we have tested the methods with field data and assessed their performance qualitatively on the basis of the insight gained from the numerical tests with synthetic data.

Download Full-text

Feature Extraction Method of Rolling Bearing Fault Based on Singular Value Decomposition-morphology Filter and Empirical Mode Decomposition

Journal of Mechanical Engineering ◽

10.3901/jme.2010.05.037 ◽

2010 ◽

Vol 46 (05) ◽

pp. 37 ◽

Cited By ~ 7

Author(s):

Baoping TANG

Keyword(s):

Feature Extraction ◽

Singular Value Decomposition ◽

Empirical Mode Decomposition ◽

Extraction Method ◽

Rolling Bearing ◽

Singular Value ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Value Decomposition ◽

Morphology Filter

Download Full-text

Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition

Applied Sciences ◽

10.3390/app10041409 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1409

Author(s):

Gang Zhang ◽

Benben Xu ◽

Kaoshe Zhang ◽

Jinwang Hou ◽

Tuo Xie ◽

...

Keyword(s):

Singular Value Decomposition ◽

Noise Reduction ◽

Signal To Noise Ratio ◽

Noise Pollution ◽

Singular Value ◽

Reduction Model ◽

Mode Decomposition ◽

Starting Point ◽

Reduction Effect ◽

Value Decomposition

Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.

Download Full-text

Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406217715483 ◽

2017 ◽

Vol 232 (12) ◽

pp. 2280-2296 ◽

Cited By ~ 3

Author(s):

Shuiguang Tong ◽

Yidong Zhang ◽

Jian Xu ◽

Feiyun Cong

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Singular Value Decomposition ◽

Empirical Mode Decomposition ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Singular Value ◽

Fault Classification ◽

Mode Decomposition ◽

Value Decomposition

In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fault types (FT), fault severities (FS), and fault loads (FL). In this study, the E–S method is adopted to analyze the simulated signals. And the experiment further discusses three cases of different FT, FS, and FL separately under six different classifiers. The experimental results show that different fault classes can be effectively distinguished by the proposed E–S in comparison with other traditional feature extraction methods. Hence, the proposed method is verified to have an effective and excellent performance in bearing fault classification.

Download Full-text

Performance Comparison of Dense Frequency Identifications for Empirical Mode Decomposition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.378-379.266 ◽

2011 ◽

Vol 378-379 ◽

pp. 266-269

Author(s):

Min Zheng ◽

Fan Shen

Keyword(s):

Singular Value Decomposition ◽

Empirical Mode Decomposition ◽

Wavelet Packet ◽

Performance Comparison ◽

Wavelet Packet Transform ◽

Singular Value ◽

Simulated Signal ◽

Mode Decomposition ◽

Frequency Components ◽

Value Decomposition

Empirical Mode Decomposition(EMD) suffers some difficulties in separating dense frequencies. The Wavelet Packet Transform (WPT) and Singular-Value Decomposition (SVD) as signal preprocessors were used to decompose a simulated signal with dense frequency components and the performances of two signal preprocess technologies were compared in this paper. The results show that Singular-Value Decomposition (SVD) as preprocessor was better in separating dense frequencies than Wavelet Packet Transform (WPT).

Download Full-text

Data-Driven Acceleration of Thermal Radiation Transfer Calculations with the Dynamic Mode Decomposition and a Sequential Singular Value Decomposition

Journal of Computational Physics ◽

10.1016/j.jcp.2021.110756 ◽

2021 ◽

pp. 110756

Author(s):

Ryan G. McClarren ◽

Terry S. Haut

Keyword(s):

Singular Value Decomposition ◽

Thermal Radiation ◽

Radiation Transfer ◽

Singular Value ◽

Dynamic Mode Decomposition ◽

Data Driven ◽

Dynamic Mode ◽

Mode Decomposition ◽

Value Decomposition

Download Full-text

Bearing Fault Detection Based on SVD and EMD

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.184-185.70 ◽

2012 ◽

Vol 184-185 ◽

pp. 70-74 ◽

Cited By ~ 3

Author(s):

Yan Long Chen ◽

Pei Lin Zhang

Keyword(s):

Singular Value Decomposition ◽

Spectrum Analysis ◽

Characteristic Frequency ◽

Singular Value ◽

Intrinsic Mode Functions ◽

Bearing Fault ◽

Mode Decomposition ◽

Signal Characteristics ◽

Value Decomposition ◽

Fault Characteristic

While bearing fault signals are strongly interferenced by noise, diagnosis using EMD directly for bearings fault becomes incorrect. A scheme based on Singular Value Decomposition(SVD) and Empirical Mode Decomposition(EMD) is proposed for solving this problem. Aiming at bearing fault signal characteristics, SVD preprocesses sampled signals to denoise. Then preprocessed signals are analyzed by EMD. Fault characteristic frequency can be obtained by spectrum analysis for Intrinsic Mode Functions(IMFs). This method is useful to detect fault of bearings and a comparison is made between it and EMD. The results show that this scheme can diagnose fault correctly under strong noise.

Download Full-text

A Feature Extraction Method of Gear Fault Based on the SVD EMD and Morphology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.477 ◽

2013 ◽

Vol 433-435 ◽

pp. 477-482 ◽

Cited By ~ 1

Author(s):

Gao Yan Hou ◽

Yong Lv ◽

Hao Huang ◽

Yi Zhu

Keyword(s):

Singular Value Decomposition ◽

Empirical Mode Decomposition ◽

Extraction Method ◽

Singular Value ◽

Background Signal ◽

Feature Extraction Method ◽

Filtering Method ◽

Mode Decomposition ◽

Gear Fault ◽

Value Decomposition

In order to extract the weak signal from strong background signal characteristics, a feature extraction method combined of the singular value decomposition (SVD), empirical mode decomposition (EMD) and mathematical morphology was proposed. The signal got through the singular value decomposition first. Next took the average value of the decomposed main components. And carried on the empirical mode decomposition and selected the main component to summate and refactor. Then morphological difference filter was used to extract the frequency characteristics of the fault signal. The results of numerical simulation test and gear fault simulation experiments show that the proposed method can clearly extract the frequency characteristics of weak signal from strong background signal and noise. Comparison has been done with the results of singular value decomposition (SVD) and morphological filtering method and empirical mode decomposition form of filtering method. It proves the effectiveness of the proposed method.

Download Full-text