scholarly journals Fault Diagnosis of Rolling Bearing Based on Wavelet Package Transform and Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 5 ◽  
pp. 792584 ◽  
Author(s):  
Quan Liu ◽  
Fen Chen ◽  
Zude Zhou ◽  
Qin Wei
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition

scholarly journals An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

2014 ◽  
Vol 6 ◽  
pp. 676205 ◽  
Author(s):  
Meijiao Li ◽  
Huaqing Wang ◽  
Gang Tang ◽  
Hongfang Yuan ◽  
Yang Yang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Outer Race ◽  
Mode Decomposition ◽  
Acceleration Sensors

In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peiming Shi ◽  
Cuijiao Su ◽  
Dongying Han
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Good Effect ◽  
Resonance System ◽  
Mode Decomposition ◽  
Eemd Method

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.

The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

2013 ◽  
Vol 300-301 ◽  
pp. 344-350 ◽  
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Jin Chuan Han
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Eemd Method

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

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