scholarly journals Vibration-Based Signal Analysis for Shearer Cutting Status Recognition Based on Local Mean Decomposition and Fuzzy C-Means Clustering

2017 ◽  
Vol 7 (2) ◽  
pp. 164 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Xinhua Liu
Keyword(s):  
Signal Analysis ◽  
Local Mean Decomposition ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Local Mean ◽  
Status Recognition

scholarly journals Improved ensemble local mean decomposition based on cubic trigonometric cardinal spline interpolation and its application for rotating machinery fault diagnosis

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094195
Author(s):  
Pei Chen ◽  
Huanguo Chen ◽  
Wenhua Chen ◽  
Jun Pan ◽  
Jianmin Li ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Spline Interpolation ◽  
Moving Average ◽  
Local Mean Decomposition ◽  
Advantages And Disadvantages ◽  
Cardinal Spline ◽  
Controlling Parameter ◽  
Local Mean ◽  
Shape Controlling ◽  
Cardinal Spline Interpolation

Ensemble local mean decomposition has been gradually introduced into mechanical vibration signal processing due to its excellent performance in electroencephalogram signal analysis. However, an unsatisfactory problem is that ensemble local mean decomposition cannot effectively process vibration signals of complex mechanical system due to the constraints of moving average. The process of moving average is time-consuming and inaccurate in complex signal analysis. Therefore, an improved ensemble local mean decomposition method called C-ELMD with modified envelope algorithm based on cubic trigonometric cardinal spline interpolation is proposed in this article. First, the shortcomings in sifting process of ensemble local mean decomposition is discussed and, furthermore, advantages and disadvantages of the common interpolation methods adopted to improve ensemble local mean decomposition are compared. Then, cubic trigonometric cardinal spline interpolation is employed to construct the local mean and envelope curves in a more precise way. In addition, the influence of shape-controlling parameter on envelope estimation accuracy in cubic trigonometric cardinal spline interpolation is also discussed in detail to select an optimal shape-controlling parameter. The effectiveness of cubic trigonometric cardinal spline interpolation for improving the accuracy of ensemble local mean decomposition is demonstrated using a synthetic signal. Finally, the proposed cubic trigonometric cardinal spline interpolation is tested to be effective in gear and bearing fault detection and diagnosis.

Fault Diagnosis of Offshore Platforms Using the Local Mean Decomposition Method

2011 ◽  
Vol 365 ◽  
pp. 94-97
Author(s):  
Jin Shan Lin
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Offshore Platforms ◽  
Local Mean Decomposition ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Local Mean ◽  
Nonlinear Signal ◽  
Nonlinear Signal Analysis ◽  
Emd Method

Traditional techniques are not suitable for exploring non-stationary and nonlinear signals. Although empirical mode decomposition (EMD) is a powerful tool for the non-stationary and nonlinear signal analysis, yet it still has some shortcomings. Local mean decomposition (LMD), a novel signal processing method, seemingly overcomes many deficiencies of the EMD method and can take place of the EMD method for analyzing non-stationary and nonlinear signals. In this paper, the LMD method is employed to examine the signal captured from the decks of the WZ12-1 platform and succeeds in displaying the reasons causing the excessive vibration of the WZ12-1 platform. The results suggest that the LMD method seems to be a feasible method for fault diagnosis of offshore platforms.

scholarly journals Brain Tumor Image Segmentation Using Kernel Based Fuzzy C Means Clustering (KFCM) Algorithm

2020 ◽  
pp. 14-18
Keyword(s):  
Good Accuracy ◽  
Median Filter ◽  
Brain Image ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Mean Filter ◽  
Fuzzy C Means Clustering ◽  
Local Mean ◽  
Non Local ◽  
Mr Brain

The kernel based fuzzy c means clustering is proposed in this article for segmentation of MR brain image. To alleviate the problem of drawback of computation cost of segmentation in the Fuzzy C Means is overcome by this kernel based FCM algorithm. The FCM algorithm provides good accuracy in the absence of noise; but in the presence of noise it doesn’t give good accuracy. In Kernal Based Fuzzy C Means, First, Enhanced Non Local mean Filter is applied on MR brain image for removal of noise and it replace the gray scale of the denoised image by the average, median filter. The Gaussian Radial basis function is used as a kernel function instead of Euclidean distance.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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