• Aucun résultat trouvé

Bearing Fault Diagnosis Using De-noising Techniques Based on EMD Combined with Coefficients Correlation

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Bearing Fault Diagnosis Using De-noising Techniques Based on EMD Combined with Coefficients Correlation"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Bearing Fault Diagnosis Using De-noising Techniques Based on EMD Combined with

Coefficients Correlation

Omar Fethi Benaouda1, Rabah Abdelkader1,2, Abdelhafid Kaddour3, Ziane Derouiche4

1,2Research Center in Industriel Technologies CRTI, p.o.box 64, Chéraga 16014 Algiers,Algeria.

2,3,4

Laboratory of Signals, Systems and Data (LSSD),Department of Electronic, University of Science and Technology, USTO-MB, Oran, Al- geria

Abstract- The vibration signals of a rolling bearing contain important information which can be used in early de- fect detection and diagnosis. This signal is usually noisy and the information about the fault can be lost. In this pa- per, a new denoising method based on Empirical Mode Decomposition Iterative Interval Thresholding and estima- tion of correlation coefficient (EMD-corIIT) is presented. The results are compared with others proposed methods.

The application of this technique to the experimental results shows that this method can extract effectively the fault features of rolling bearing compared with the others cited methods.

Keywords: Denoising, EMD, Thresholding, Vibration Signal, Bearing Fault, Correlation Coefficients

Références

Télécharger maintenant ( PDF - 1 Page - 12.10 KB )

Documents relatifs

Bearing fault diagnosis based on feature extraction and condition classification

In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of signal's time-varying statistical parameters and features obtained through

Bearing fault diagnosis based on feature extraction and condition classification

In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of signal's time-varying statistical parameters and features

Rolling bearing faults diagnosis based onempirical mode decomposition: Optimizedthreshold de-noising method

Secondly the algorithm based on the energy to determine the trip point is designed for IMF selection, then, by comparing the energy of the selected IMFs with excluded IMFs,

Rolling Bearing Faults Diagnosis Based on Empirical Mode Decomposition: Optimized Threshold De-noising Method

Firstly, the signal is decomposed into a number of IMFsusing the EMD decomposition .Secondly the algorithm based on the energy to determine the trip point is designed for

Faults Diagnosis of Rolling Bearing Based onEmpirical Mode Decomposition: OptimizedThreshold De-noising Method

Firstly, the signal is decomposed into a number of IMFs using the EMD decomposition .Secondly the algorithm based on the energy to determine the trip point is designed for

Faults Diagnosis of Rolling Bearing Based on Empirical Mode Decomposition: Optimized Threshold De-noising Method

Firstly, the signal is decomposed into a number of IMFs using the EMD decomposition .Secondly the algorithm based on the energy to determine the trip point is designed

Bearing Fault Diagnosis Using De-noising Techniques Based on EMD Combined with Coefficients Correlation

The application of this technique to the experimental results shows that this method can extract effectively the fault features of rolling bearing compared with the others

A Feature Selection Method based on Tree Decomposition of Correlation Graph

Compared to existing graph- based feature selection methods, our method exploits tree decomposition techniques to reveal groups of highly correlated features.. FSTDCG