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

2018 ◽  
Vol 18 (2) ◽  
pp. 2_95-2_114
Author(s):  
Nobuoto NOJIMA ◽  
Masumitsu KUSE ◽  
LE QUANG DUC
Keyword(s):  
Singular Value Decomposition ◽  
Ground Motion ◽  
Strong Ground Motion ◽  
Singular Value ◽  
Mode Decomposition ◽  
Value Decomposition ◽  
Strong Ground

Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition

2017 ◽  
Vol 46 (12) ◽  
pp. 1201003
Author(s):  
程知 CHENG Zhi ◽  
何枫 HE Feng ◽  
靖旭 JING Xu ◽  
张巳龙 ZHANG Si-long ◽  
侯再红 HOU Zai-hong
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Lidar Signal ◽  
Mode Decomposition ◽  
Value Decomposition

Microseismic denoising assessment by polarization histograms

Geophysics ◽  
2020 ◽  
pp. 1-46
Author(s):  
German I. Brunini ◽  
Juan I. Sabbione ◽  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Singular Value Decomposition ◽  
Quality Factor ◽  
Field Data ◽  
Synthetic Data ◽  
Singular Value ◽  
Mode Decomposition ◽  
Actual Field ◽  
Band Pass ◽  
Value Decomposition ◽  
Better Than

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.

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

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.

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

2017 ◽  
Vol 232 (12) ◽  
pp. 2280-2296 ◽  
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.

Performance Comparison of Dense Frequency Identifications for Empirical Mode Decomposition

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).

Bearing Fault Detection Based on SVD and EMD

2012 ◽  
Vol 184-185 ◽  
pp. 70-74 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document