Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models

Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models

The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently...

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Bibliographic Details
Main Authors: Alexander E. Prosvirin, Manjurul Islam, Jaeyoung Kim, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Sensors
Subjects:
degree-of-presence ratio
ensemble empirical mode decomposition
fault diagnosis
feature extraction
intrinsic mode function selection
Kullback–Leibler divergence
rotating machinery
rub-impact faults
Online Access:http://www.mdpi.com/1424-8220/18/7/2040

Internet

http://www.mdpi.com/1424-8220/18/7/2040

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