scholarly journals Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

10.5772/67530 ◽  
2017 ◽  
Author(s):  
Wei Guo ◽  
Ming J. Zuo
Keyword(s):  
Decomposition Methods ◽  
Rotating Machinery ◽  
Signal Decomposition ◽  
Vibration Signals ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal

scholarly journals A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

2019 ◽  
Vol 255 ◽  
pp. 02017 ◽  
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
M. H. Lim ◽  
M. K. Zakaria
Keyword(s):  
Decomposition Method ◽  
Rotating Machinery ◽  
Signal Decomposition ◽  
Future Research ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
Mixing Problem ◽  
Conventional Signal

Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis.

scholarly journals Study on Frequency Characteristics of Rotor Systems for Fault Detection Using Variational Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Li Qin
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Rotor Systems ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Nonlinear Features ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal

Due to the complicated structure, vibration signal of rotating machinery is multicomponent with nonstationary and nonlinear features, so it is difficult to diagnose faults effectively. Therefore, effective extraction of vibration signal characteristics is the key to diagnose the faults of rotating machinery. Mode mixing and illusive components existed in some conventional methods, such as EMD and EEMD, which leads to misdiagnosis in extracting signals. Given these reasons, a new fault diagnosis method, namely, variation mode decomposition (VMD), was proposed in this paper. VMD is a newly developed technique for adaptive signal decomposition, which can decompose a multicomponent signal into a series of quasi-orthogonal intrinsic mode functions (IMFs) simultaneously, corresponding to the components of signal clearly. To further research on VMD method, the advantages and characteristics of VMD are investigated via numerical simulations. VMD is then applied to detect oil whirl and oil whip for rotor systems fault diagnosis via practical vibration signal. The experimental results demonstrate the effectiveness of VMD method.

Fault Identification for Rotating Machinery Using Adaptive Signal Processing and Time-Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Signal Processing ◽  
Background Noise ◽  
Combustion Engine ◽  
Adaptive Signal Processing ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Abstract Impulsive sound and vibration signals in rotating machinery are often associated with faults which lead to due to irregular impacting. Thus these impulsive sound and vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive sounds embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within internal combustion engine and industrial gear.

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