scholarly journals Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1210 ◽  
Author(s):  
Rui Yuan ◽  
Yong Lv ◽  
Gangbing Song
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Singular Value ◽  
High Order ◽  
Rolling Bearings ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Value Decomposition

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

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).

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

2013 ◽  
Vol 433-435 ◽  
pp. 477-482 ◽  
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.

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