• Aucun résultat trouvé

FAULT DIAGNOSIS OF ROTATINGMACHINERY USING WAVELETTRANSFORM AND PRINCIPALCOMPONENT ANALYSIS

N/A
N/A
Protected

Academic year: 2021

Partager "FAULT DIAGNOSIS OF ROTATINGMACHINERY USING WAVELETTRANSFORM AND PRINCIPALCOMPONENT ANALYSIS"

Copied!
1
0
0

Texte intégral

(1)

International Arab Conference on Information Technology (ACIT) 2010

FAULT DIAGNOSIS OF ROTATING MACHINERY USING WAVELET

TRANSFORM AND PRINCIPAL COMPONENT ANALYSIS

Hocine BENDJAMA, Mohamad S. Boucherit, Salah Bouhouche

Abstract : Fault diagnosis is playing today a crucial role in industrial systems. To improve the reliability, safety and efficiency advanced methods of fault diagnosis become increasingly important for many systems. In this paper, fault diagnosis of rotating machinery is performed using a combination between Wavelet Transform (WT) and Principal Component Analysis (PCA) methods.

The WT is employed to decompose the vibration signal of measurements data in different frequency bands. The obtained decomposition levels are used as input to the PCA method for fault detection and diagnosis. The objective of this method is to obtain the information contained in the frequency bands of the measured data. The proposed method is evaluated using experimental measurements data with mass unbalance and gear fault.

Keywords : Vibration measurement, Fault Diagnosis, Wavelet Analysis, principal component analysis, Mass Unbalance, Gear Fault

Centre de Recherche en Technologies Industrielles - CRTI - www.crti.dz

Powered by TCPDF (www.tcpdf.org)

Références

Documents relatifs

The measurement of vibration applied to condition monitoring and fault diagnosis requires different types of equipment and techniques. These depend on the investment and

Keywords : fault detection and diagnosis, principal component analysis, multivariate statistical process control, T2-statistic, Q-statistic, squared prediction error,

Keywords: fault detection and diagnosis, principal component analysis, multivariate statistical process control, T 2 -statistic, Q-statistic, squared prediction

In this paper, a new combined fault diagnosis method that uses Wavelet Transform (WT), Principal Component Analysis (PCA) and Neural Networks (NN) is proposed for rotating

In this paper, a new combined fault diagnosis method that uses Wavelet Transform (WT), Principal Component Analysis (PCA) and Neural Networks (NN) is proposed

The physical motion of rotating machines generates vibration and with the advances in digital signal processing research, there has been an increasingly strong

On the other hand, vibration monitoring is reported as an interesting technique for the rotating machinery condition diagnosis.This paper considers the Wavelet Transform (WT) and FFT

In this work, the WT is used to filter useless frequencies while keeping the useful band of frequencies of the analyzed signal (A3) and the approximation 3 (A3) is used to