Multi-Scale Fault Frequency Extraction Method Based on EEMD for Slewing Bearing Fault Diagnosis

Yang, Jie; Chen, Jie; Hong, Rongjing; Wang, Hua

doi:10.1007/978-3-319-13707-0_40

Cited by 2 publications

(1 citation statement)

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“…Moreover, EEMD has attracted significant attention in fault diagnoses domain. For example, Wang [14] proposed a method based on multi-scale principal component analysis to extract fault frequency of slewing bearing. Chen [15] improved EEMD by adding white noises and adaptively determining an ensemble number to extract fault features.…”

Section: Introductionmentioning

confidence: 99%

Novel complete ensemble EMD with adaptive noise-based hybrid filtering for rolling bearing fault diagnosis

Song¹,

Sun²,

Zhan³

2019

J. vibroeng.

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A feature extraction of fault bearing has attracted considerable attention in recent years. However, weak fault feature is difficult to extract under heavy background noise. To solve this problem, a novel multi-layer filtering method is proposed to filter out noise and extract weak fault feature. The first layer introduces a metric based on de-trended fluctuation analysis (DFA) to identify intrinsic mode function (IMF) that reflect period impulsive information for vibration signal adaptively. The second layer uses non-local mean (NLM) method as a pre-filter of the third layer to realize extraction of singular value decomposition (SVD) which reflect the most information of IMFs. The last layer introduces a relative energy difference criterion of a singular value to extract important feature of Hankel matrix of IMFs. The filtered signal is obtained by reconstructed signal from identified singular value of SVD. Experiment results on simulation and real vibration signals indicate that the hybrid filtering method removes heavy noise successfully and extract weak fault feature of rolling bearing effectively.

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Section: Introductionmentioning

confidence: 99%