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Bearing Fault Diagnosis Using Empirical Mode Decomposition, Entropy Based Features And Data Mining Techniques
Reddy M.S.P, Reddy D.M, ,
Published in Elsevier BV
Volume: 5
Issue: 5
Pages: 11460 - 11475
Rolling bearing is an important part of rotating machinery. Working condition of bearing will directly affect the operation and function of equipment. Therefore, the testing and fault diagnosis of the running state of bearing is significant. A common method for extracting the mechanical fault is assuming that vibration signals are stable. But the real measured signals often are non-stationary. For this nonlinear phenomenon, it is difficult to solve the problem fundamentally by the traditional power spectrum analysis.In this present work aims to formulate an automated prediction model using vibration signals of various bearing conditions by using EMD (empirical mode decomposition) and entropy based features and different classification algorithms. © 2017 Elsevier Ltd.
About the journal
JournalData powered by TypesetMaterials Today: Proceedings
PublisherData powered by TypesetElsevier BV
Open AccessNo