• Aucun résultat trouvé

Condition Monitoring of Rotating MachinesUsing BayesianTrained Artificial NeuralNetworks

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Condition Monitoring of Rotating MachinesUsing BayesianTrained Artificial NeuralNetworks"

Copied!
1
0
0

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

Texte intégral

(1)

7th International Conference on Electrical Engineering 2012

Condition Monitoring of Rotating Machines Using BayesianTrained Artificial Neural

Networks

A. Boudiaf A. K. Moussaoui

Abstract : The paper deals with the application of Artificial Neural Network (ANN) for the prediction of the effect ofunbalance fault on the frequency components of vibration signature of rotating machines. The suggested Technique isapplied to real vibratory signals resulting from sensors placed on a test rig interfaced to a multi-channel dataacquisition system OROS 25. Several statistical characteristic features of frequency domain vibration signals havebeen used as inputs to the ANN. The suggested ANN prediction model was implemented using Bayesian Evidencebased training algorithm knowing for its established efficiency comparatively to other learning techniques. Theperformance of the suggested condition monitoring scheme was confirmed by the accurate results of detection ofunbalance fault signals in the considered rotating machine.

Keywords : Condition monitoring, Fault Diagnosis, Artificial Neural Networks, Bayesian learning, Vibration analysis, unbalance, Rotating machines

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

Powered by TCPDF (www.tcpdf.org)

Références

Télécharger maintenant ( PDF - 1 Page - 7.80 KB )

Documents relatifs

Condition Monitoring of RotatingMachinesUsing Artificial Neural NetworksandWavelet Transform

The suggested Technique is applied to realvibratory signals resulting from sensors placed on a test riginterfaced to a multi-channel data acquisition system OROS 25.The

Condition Monitoring of Rotating Machines Using Artificial Neural Networks and Wavelet Transform

Abstract— This paper deals with the application of Artificial Neural Network (ANN) and Wavelet Transform (WT) for the prediction of the effect of unbalance fault on the

Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model

Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model.. Yoann Altmann, Nicolas Dobigeon,

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

TAG_2005 TAG_2006 TAG_2007 TAG_agdetection TAG_algorithms TAG_amperometry TAG_analysis TAG_and TAG_annotation TAG_antibody TAG_apob TAG_architecture TAG_article

A computationally efficient algorithm for genomic prediction using a Bayesian model

We compared the speed of emBayesR with BayesR and fastBayesB using three criteria: the time complexity of each iteration (the function in terms of number of SNPs and individuals

Improving Energy Efficiency in the Production Floor Using SoA-Based Monitoring Techniques

The procedure of developing a virtual smart device in the Delmia Automation environment with a DPWS-based service interface which is identical to the one

Condition monitoring of rotating machinery byvibration signal processing methods

Durant la dernière décennie, des recherches actives et considérables se sont effectuées en vue de développer des mé-thodes de détection et de diagnostic de défauts des

Condition monitoring of rotating machinery by vibration signal processing methods

The result of db4 decomposition of the vibration signal of the mass unbalance collected at a rota- tion frequency of 15 Hz is given in Fig.7.. Obviously, there is no