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Using Multivariate Statistics & Multi-ScaleEntropy to Monitor Gears Degradation andSignal Denoising Strategy using WaveletDecomposition

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Academic year: 2021

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7th African Conference on Non Destructive Testing ACNDT 2016 & the 5th International Conference on NDT and Materials Industry and Alloys (IC-WNDT-MI)

2016

Using Multivariate Statistics & Multi-Scale Entropy to Monitor Gears Degradation and

Signal Denoising Strategy using Wavelet Decomposition

S. AOUABDI

Abstract : This paper focuses on fault diagnosis in gears transmissions driven by induction machines. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm in conjunction with multivariate statistical approach of the motor current signature analysis (MCSA) is proposed. Simulation results show that the proposed methods are able to detect tooth surface decay in the permanent regime of MCSA.

Keywords : gear Fault diagnosis, Multi-scale entropy, Induction machine, Wavelet decomposition and principal component analysis.

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

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