A Periodic Noise Separation Method Research of well Field Three-Dimensional Drill String Vibration Signal

2013 ◽  
Vol 318 ◽  
pp. 27-32
Author(s):  
Hao Cheng Wu ◽  
Yong Shou Dai ◽  
Wei Feng Sun ◽  
Li Gang Li ◽  
Ya Nan Zhang
Keyword(s):  
Independent Component Analysis ◽  
Vibration Signal ◽  
Component Analysis ◽  
Drill String ◽  
Independent Component ◽  
Analysis Algorithm ◽  
String Vibration ◽  
Periodic Noise ◽  
Well Field ◽  
Independent Component Analysis Algorithm

Periodic noise is an important manifestation of the drill string vibration signal noise. In order to extract the characteristics of the signals which reflect the situation of the tools in drilling, the periodic components which influence the original drill string vibration signal in the well field were researched and the independent component analysis algorithm which is on the basis of negative entropy for periodic vibration noise separation was adopted. At the same time, the effect of algorithm demixing was improved where periodic noise components which existed in three directions of drill string vibration signals were used, combining with the improved particle swarm optimization algorithm to seek the optimal mixed matrix by which the multi-channel mixed-signal of independent component analysis algorithm could be structured. This method in operation was fast. And after separation each signal was of high similarity. Through the experimental simulation, the method was proven effective in the drill string vibration periodic noise signal separation.

scholarly journals Reduction of Multidimensional Image Characteristics Based on Improved KICA

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Jia Dongyao ◽  
Ai Yanke ◽  
Zou Shengxiong
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Principal Component ◽  
Characteristic Parameter ◽  
Component Analysis ◽  
Independent Component ◽  
Average Correlation ◽  
Analysis Algorithm ◽  
Correlation Degree ◽  
Kernel Independent Component Analysis

The domestic and overseas studies of redundant multifeatures and noise in dimension reduction are insufficient, and the efficiency and accuracy are low. Dimensionality reduction and optimization of characteristic parameter model based on improved kernel independent component analysis are proposed in this paper; the independent primitives are obtained by KICA (kernel independent component analysis) algorithm to construct an independent group subspace, while using 2DPCA (2D principal component analysis) algorithm to complete the second order related to data and further reduce the dimension in the above method. Meanwhile, the optimization effect evaluation method based on Amari error and average correlation degree is presented in this paper. Comparative simulation experiments show that the Amari error is less than 6%, the average correlation degree is stable at 97% or more, and the parameter optimization method can effectively reduce the dimension of multidimensional characteristic parameters.

Elimination of Backscatter Noise of Underwater LiDAR Using Independent Component Analysis Algorithm

2021 ◽  
Vol 41 (4) ◽  
pp. 0401004
Author(s):  
齐若伊 Qi Ruoyi ◽  
李坤 Li Kun ◽  
杨苏辉 Yang Suhui ◽  
高彦泽 Gao Yanze ◽  
王欣 Wang Xin ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Analysis Algorithm ◽  
Underwater Lidar ◽  
Independent Component Analysis Algorithm
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