Oscillatory hyper Hilbert transforms along general curves

Chen, Jiecheng; Mitiku, Belay; Xiang-rong, Zhu

doi:10.1007/s11464-016-0574-3

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…As a new method proposed by Tsinghua University, the robust and flexible morphological filtering technology was first applied to the defect diagnosis of rolling bearings under strong noise background. is method can efficiently extract the signal shape and edge contour features and is of great reference value [23]. e research team of Professor Zhou Zude of Wuhan University of Technology has applied the HHT analysis method to the detection and diagnosis of bearing vibration faults in rotating machinery equipment, extracting state characteristic signals under severe working conditions, and has achieved a series of scientific research results [24,25].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Vibration Signal Feature Extraction and Recognition Method Based on EEMD Superresolution Sparse Decomposition

Zhang

Raja

Deng³

et al. 2022

Shock and Vibration

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This paper aims at the shortcomings of the current conventional processing methods of bearing fault vibration signals in improving signal-to-noise ratio, fine feature extraction, and recognition. A feature extraction and recognition method of abnormal vibration signals based on Ensemble Empirical Mode Decomposition (EEMD) superresolution sparse decomposition is designed. First of all, the superresolution sparse decomposition method is used to refine the set of IMF components of vibration signals after EEMD decomposition. Secondly, the features of the set are extracted and their corresponding energy entropy is calculated. Thirdly, the classification and recognition are carried out. Finally, the effectiveness and feasibility of the method are verified by experiments. It has been proved that this method can better realize the denoising and fine processing aimed at abnormal vibration signals. It has certain theoretical significance and applied value.

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Section: Introductionmentioning

confidence: 99%