Study on Rolling Element Bearing Fault Diagnosis Methods Based on Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 457-458 ◽  
pp. 602-607
Author(s):  
Zhong Liang Lv ◽  
Yi Lin Liu ◽  
Xian Wu Han ◽  
Min Liu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Coefficient Method

For rotating machinery, a fault diagnosis method is proposed on the basis of the EEMD (Ensemle Emperical Mode Decomposition) and Correlation Coefficient Method. In the Vibration Signals and rotate speed of rotating machinery, the fault diagnosis method is achieved by Spectrum Analysis and real-time monitoring. The original signals are decomposed into several IMF components. Each IMF contains the local feature of the signal. Correlation coefficient method is used to select the appropriate Intrinsic Mode Function. Extract the fault feature through its envelope diagram. Experiment proves the feasibility of this method.

scholarly journals A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Chenguang Huang ◽  
Jianhui Lin ◽  
Jianming Ding ◽  
Yan Huang
Keyword(s):  
Fault Diagnosis ◽  
Singular Values ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Periodic Impulse ◽  
Value Decomposition

A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.

A fault diagnosis method of rolling element bearings based on CEEMDAN

2015 ◽  
Vol 231 (10) ◽  
pp. 1804-1815 ◽  
Author(s):  
Yaguo Lei ◽  
Zongyao Liu ◽  
Julien Ouazri ◽  
Jing Lin
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Noisy Signals ◽  
Diagnosis Method

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

scholarly journals Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1693 ◽  
Author(s):  
Lanjun Wan ◽  
Yiwei Chen ◽  
Hongyang Li ◽  
Changyun Li
Keyword(s):  
Fault Diagnosis ◽  
Convergence Speed ◽  
Rolling Element Bearing ◽  
Fault Classification ◽  
Generalization Ability ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Element Bearing

To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.

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