scholarly journals Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach

Entropy ◽  
2017 ◽  
Vol 19 (7) ◽  
pp. 317 ◽  
Author(s):  
Qing Li ◽  
Xia Ji ◽  
Steven Y. Liang
Keyword(s):  
Feature Extraction ◽  
Rotating Machinery ◽  
Variational Mode Decomposition ◽  
Minimum Entropy ◽  
Decomposition Approach ◽  
Mode Decomposition ◽  
Incipient Fault ◽  
Fault Feature Extraction

Fault diagnosis of rolling bearing’s early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm

2014 ◽  
Vol 229 (16) ◽  
pp. 2890-2907 ◽  
Author(s):  
Hongchao Wang ◽  
Jin Chen ◽  
Guangming Dong
Keyword(s):  
Feature Extraction ◽  
Early Stage ◽  
Ensemble Empirical Mode Decomposition ◽  
Minimum Entropy ◽  
Signal Attenuation ◽  
Mode Decomposition ◽  
Weak Fault ◽  
Simulation Signal ◽  
Environment Noise ◽  
Fault Feature Extraction

The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, frequency slice wavelet transformation and ensemble empirical mode decomposition. The effectiveness and advantages of the proposed method are verified through simulation signal and experiment.

Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition

2019 ◽  
Vol 234 (6) ◽  
pp. 848-861 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Shuai Wang ◽  
Yiqun Qian
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Variational Mode Decomposition ◽  
Variable Speed ◽  
Noise Interference ◽  
Order Spectrum ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Non Stationary Signal ◽  
Fault Feature Extraction

This paper proposes a study on gearbox fault feature extraction of wind turbine under variable speed condition using improved adaptive variational mode decomposition (VMD). Frequent changes in wind speed and critical noise interference make the vibration signal exhibit non-stationary characteristics. Although computational order tracking can transform non-stationary signals in the time domain into stationary angular signals, and then extract fault features from order spectrum obtained by FFT, the obtained order spectrum is liable to be polluted by noise or to appear order aliasing. To avoid order aliasing and eliminate noise interference, the original non-stationary signal is firstly processed using the improved adaptive VMD method called adaptive differential evolution – VMD (ADE-VMD). ADE-VMD can not only utilise the advantages of traditional VMD but also adaptively select narrow-band intrinsic mode function (NBIMF) to construct the reconstructed signal with less noise and without order aliasing. In the experiment, we compared the ADE-VMD method with other VMD methods such as GA-VMD, PSO-VMD and DE-VMD, and the results showed that ADE-VMD has excellent adaptive processing ability, and its convergence and optimisation speed are more remarkable. ADE-VMD can effectively filter the noise inference and avoid the order aliasing, so it is well suitable for fault feature extraction under variable speed.

scholarly journals An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Jiancheng Gong ◽  
Xiaoqiang Yang ◽  
Fan Pan ◽  
Wuqiang Liu ◽  
Fuming Zhou
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Coarse Grained ◽  
Normal Operation ◽  
Permutation Entropy ◽  
High Dimensional ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Fault Feature Extraction

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

An optimal variational mode decomposition for rolling bearing fault feature extraction

2019 ◽  
Vol 30 (5) ◽  
pp. 055004 ◽  
Author(s):  
Dongdong Wei ◽  
Hongkai Jiang ◽  
Haidong Shao ◽  
Xingqiu Li ◽  
Ying Lin
Keyword(s):  
Feature Extraction ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction
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