scholarly journals Fault Feature Extraction of Rolling Bearings Using Local Mean Decomposition-Based Enhanced Sparse Coding Shrinkage

2021 ◽  
Author(s):  
Yuanhang Sun ◽  
Jianbo Yu
Keyword(s):  
Feature Extraction ◽  
Sparse Coding ◽  
Rolling Bearings ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Fault Feature Extraction

scholarly journals The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Yugang Fan ◽  
Xiaodong Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Dominant Frequency ◽  
Working Process ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Common Time ◽  
Local Mean ◽  
Fault Feature Extraction

Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

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