Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine

Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine

Multi-fault diagnosis of rolling element bearing is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of nonstationarity and nonlinearity, the detection, extraction and classification of the fault feature turn into a...

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Bibliographic Details
Main Authors: Qingbin Tong, Baozhu Han, Yuyi Lin, Weidong Zhang
Format: Article
Language:English
Published: JVE International 2016-11-01
Series:Journal of Vibroengineering
Subjects:
fault diagnosis
redundant second generation wavelet packet transform (RSGWPT)
ensemble empirical mode decomposition (EEMD)
intrinsic mode functions (IMFs)
energy ratio
singular value decomposition (SVD)
least squares support vector machine (LSSVM)
particle swarm optimization (PSO)
Online Access:https://www.jvejournals.com/article/17090

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https://www.jvejournals.com/article/17090

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