Extraction of Gravity Anomalies Associated with Gold Mineralization: A Comparison of Singular Value Decomposition and Bi-Dimensional Empirical Mode 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.

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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).

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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.

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Extraction of Gravity Anomalies Associated with Gold Mineralization: A Comparison of Singular Value Decomposition and Bi-Dimensional Empirical Mode Decomposition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.455-456.1567 ◽

2012 ◽

Vol 455-456 ◽

pp. 1567-1577 ◽

Cited By ~ 6

Author(s):

Yong Qing Chen ◽

Bin Bin Zhao

Keyword(s):

Gravity Anomaly ◽

Sedimentary Basin ◽

Gold Mineralization ◽

Gravity Data ◽

Gravity Anomalies ◽

Gold Deposits ◽

Singular Value ◽

Controlling Factor ◽

Mode Decomposition ◽

Value Decomposition

Two methods of both the singular value decomposition (SVD) and the Bi-dimensional empirical mode decomposition (BEMD) were applied in extraction of gravity anomalies associated with gold mineralization in Tongshi gold field, respectively in this paper. Conclusions drawn by the comparison study are as follows: (a) The ore-controlling factor in the Tongshi gold field illustrated in the images obtained from the original gravity data by the two methods is the same that the Tongshi intrusions with a negative circular gravity anomaly and the ring contact metasomatic mineralization zone around the Tongshi intrusions with the positive gravity anomaly. (b) The two methods reveal the same spatial relationship between the ore-controlling factor and various gold mineralizations that the skarn and porphyry types of gold deposits are located within the complex pluton and the Carlin and Crypto-breccia types of gold deposits located within the contact metasomatic mineralization zone. (c) The image produced by BEMD not only reflects the structural features of the ore-controlling factor (Tongshi complex pluton), but also does the distributions of the other geological units in the Tongshi gold field such as the Mesozoic volcanic sedimentary basin in NW orientation with obvious negative gravity anomaly and the conceal metamorphic base swell in NW orientation with the positive gravity anomaly located between the Tongshi intrusions and the Mesozoic volcanic sedimentary basin. The image produced by SVD might depict in more detail the inner structure of the Tongshi intrusions and the ring contact metasomatic zone than that produced by BEMD. The higher gravity anomaly areas in island shape within the ring contact metasomatic zone may be caused by the skarn bodies with iron-copper-gold mineralization. (d) Under the constraints of the ore-forming geological setting, the results obtained from the original gravity data by combination of the two methods can depict the relationships between the ore-controlling factors and the gold mineralizations more exactly than the alternative methods.

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