scholarly journals Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Jinbao Zhang ◽  
Yongqiang Zhao ◽  
Xinglin Li ◽  
Ming Liu
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Multiscale Entropy ◽  
Small Samples ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Sparse Representation Classification ◽  
Iterative Filtering

To improve the bearings diagnosis accuracy considering multiple fault types with small samples, a new approach that combined adaptive local iterative filtering (ALIF), multiscale entropy features, and kernel sparse representation classification (KSRC) is put forward in this paper. ALIF is used to adaptively decompose the nonlinear, nonstationary vibration signals into a sum of intrinsic mode functions (IMFs). Multiple entropy features such as sample entropy, fuzzy entropy, and permutation entropy with multiscale are computed from the first three IMFs and a total of one hundred and eighty features are obtained. After normalization, the features are employed to train and test the classifier KSRC, respectively. Finally, the proposed approach is evaluated with two experimental tests. One is concerned with different types of bearing faults from the centrifugal pump; and the other is from Case Western Reserve University (CWRU) considering 12 bearing fault states. Experimental results have proved that the proposed approach is efficient for bearing fault diagnosis, and high accuracy will be obtained with high dimensional features through small samples.

scholarly journals Bearings fault diagnosis based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983631 ◽  
Author(s):  
Jinbao Zhang ◽  
Yongqiang Zhao ◽  
Ming Liu ◽  
Lingxian Kong
Keyword(s):  
Fault Diagnosis ◽  
Logistic Model ◽  
Group Lasso ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Multinomial Logistic Model ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Stationary Vibration ◽  
Iterative Filtering

Bearing fault diagnosis attracts great attention because the bearing condition has direct effects on productivity and safety in industry. To accurately identify the operating condition of bearings, a novel bearing fault diagnosis method based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso is first put forward in this article. In the proposed method, adaptive local iterative filtering was applied to decompose the nonlinear and non-stationary vibration signals into intrinsic mode functions. The multiscale permutation entropy values of the first several intrinsic mode functions were calculated to characterize the complexity of intrinsic mode functions in different scales, and they constructed feature vectors after normalization. Multinomial logistic model with group-lasso could perform multiple classifications with an embedded approach for feature selection, which is distinct from the traditional methods with two steps of dimensionality reduction and classification. Finally, the proposed method was verified with experiment data from Case Western Reserve University considering four conditions: different fault types, different damages, multiple types, and different loads. The results indicate that the proposed method is effective in identifying different categories of rolling bearings.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

Permutation entropy-based 2D feature extraction for bearing fault diagnosis

2020 ◽  
Vol 102 (3) ◽  
pp. 1717-1731
Author(s):  
Mantas Landauskas ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Permutation Entropy ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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