scholarly journals An early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique

2018 ◽  
Vol 18 ◽  
pp. 41-45 ◽  
Author(s):  
Kaiming Teng ◽  
Chengcheng Zhu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Random Decrement Technique ◽  
Mode Decomposition ◽  
Random Decrement ◽  
Fault Feature Extraction

scholarly journals A Feature Extraction Method Based on VMD and RDT and Its Application in Engine Crankshaft Bearing Fault

2018 ◽  
Author(s):  
Gang Ren ◽  
Jide Jia ◽  
Jianmin Mei ◽  
Xiangyu Jia ◽  
Jiajia Han
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Random Noise ◽  
Permutation Entropy ◽  
Noise Robustness ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Random Decrement Technique ◽  
Mode Decomposition ◽  
Random Decrement

The vibration signal of the engine contains strong background noise and many kinds of modulating components, which is difficult to diagnose. Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). VMD can effectively avoid endpoint effect and modal aliasing. However, VMD cannot effectively eliminate the random noise in the signal, so the random decrement technique is introduced to solve the problem. Based on the crankshaft bearing fault simulation experiment, the four kinds of wear state vibration signals are decomposed by VMD, and the modal components with smaller permutation entropy are selected as fault components. Then the fault component is processed by the random decrement technique, and the Hilbert envelope spectrum of the fault component is obtained. Compared with the fault feature extraction method based on EMD and EEMD, the feature extraction results of the proposed method are better than those of the above two methods. The simulation analysis and the simulation test of the crankshaft bearing fault verify the effectiveness of the proposed method.

scholarly journals Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 468 ◽  
Author(s):  
Dongri Xie ◽  
Hamada Esmaiel ◽  
Haixin Sun ◽  
Jie Qi ◽  
Zeyad A. H. Qasem
Keyword(s):  
Feature Extraction ◽  
Correlation Coefficient ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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