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Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

Lilia Chaabi, Ahcene Lemzadmi, Abderrazek Djebala, Mohamed Lamine Bouhalais, Nouredine Ouelaa

2020The International Journal of Advanced Manufacturing Technology43 citationsDOI

Topics & Concepts

KurtosisWaveletPrincipal component analysisNoise reductionEnvelope (radar)SIGNAL (programming language)Fault (geology)Hilbert–Huang transformNoise (video)Pattern recognition (psychology)AlgorithmEngineeringControl theory (sociology)MathematicsArtificial intelligenceWhite noiseComputer scienceStatisticsGeologyRadarImage (mathematics)SeismologyTelecommunicationsProgramming languageControl (management)Machine Fault Diagnosis TechniquesGear and Bearing Dynamics AnalysisEngineering Diagnostics and Reliability
Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses | Litcius