A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings

2014 ◽  
Vol 75 ◽  
pp. 67-78 ◽  
Author(s):  
Huanhuan Liu ◽  
Minghong Han
Keyword(s):  
Fault Diagnosis ◽  
Local Mean Decomposition ◽  
Multi Scale ◽  
Roller Bearings ◽  
Diagnosis Method ◽  
Local Mean

scholarly journals A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

2018 ◽  
Vol 37 (4) ◽  
pp. 928-954 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Xiaodong Wang
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Local Mean Decomposition ◽  
Diagnosis Method ◽  
Local Mean ◽  
Teager Energy

Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising preprocessing method, singular value difference spectrum denoising is applied to reduce the noise of the bearing vibration signal and improve the signal-to-noise ratio. Then, local mean decomposition method is used to decompose the denoised signals into several product functions. And product functions corresponding to the fault feature are selected according to the correlation coefficient criterion. Finally, Teager energy spectrum is analyzed by applying the Teager energy operator to the constructed amplitude modulation component. The proposed method is successfully applied to analyze the vibration signals collected from an experimental motive rolling bearing and rolling bearing of the self-made rotor experimental platform. The experimental results demonstrate that the proposed singular value difference spectrum denoising and local mean decomposition method can achieve fairly or slightly better performance than the normal local mean decomposition-Teager energy operator method, fast kurtogram, and the wavelet denoising and local mean decomposition method.

Fault Diagnosis Method of Gearbox Based on Local Mean Decomposition

2013 ◽  
Vol 376 ◽  
pp. 441-445 ◽  
Author(s):  
Jian Zhang ◽  
Hui Mei Li ◽  
Yan Feng Tang ◽  
Qin Qin Wang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Linear Combination ◽  
Frequency Method ◽  
Local Mean Decomposition ◽  
Time Frequency ◽  
Finite Set ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Local Mean

Local mean decomposition(LMD),which is a new time-frequency method, can decompose a complex multicomponent modulation signal into a linear combination of a finite set of mono-component modulation signals. LMD integrates two signal processing procedures: decomposition and demodulation, and it can extract modulation feature efficiently. The basic theory and algorithm of LMD is introduced, and the effection of LMD is verified trough simulation. LMD is applied in gearbox fault diagnosis and successfully extracts modulation feature.

A new wind turbine fault diagnosis method based on the local mean decomposition

2012 ◽  
Vol 48 ◽  
pp. 411-415 ◽  
Author(s):  
W.Y. Liu ◽  
W.H. Zhang ◽  
J.G. Han ◽  
G.F. Wang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Local Mean Decomposition ◽  
Diagnosis Method ◽  
Local Mean
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