A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.

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A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

Measurement ◽

10.1016/j.measurement.2015.08.034 ◽

2016 ◽

Vol 77 ◽

pp. 80-94 ◽

Cited By ~ 136

Author(s):

Yongbo Li ◽

Minqiang Xu ◽

Yu Wei ◽

Wenhu Huang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Binary Tree ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Improved Support Vector Machine

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Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2019-0114 ◽

2020 ◽

Vol 44 (3) ◽

pp. 405-418

Author(s):

Shuzhi Gao ◽

Tianchi Li ◽

Yimin Zhang

Keyword(s):

Fault Diagnosis ◽

Roller Bearing ◽

Vibration Signal ◽

Rolling Bearing ◽

Fault Classification ◽

Kernel Principal Component Analysis ◽

Permutation Entropy ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.

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Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

Shock and Vibration ◽

10.1155/2014/154291 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 23

Author(s):

Jinde Zheng ◽

Junsheng Cheng ◽

Yu Yang

Keyword(s):

Experimental Data ◽

Fault Diagnosis ◽

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Laplacian Score ◽

Definition Of

A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

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Bearing Fault Diagnosis Method Based on EEMD and LSTM

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2020.1.3780 ◽

2020 ◽

Vol 15 (1) ◽

Cited By ~ 2

Author(s):

Ping Zou ◽

Baocun Hou ◽

Jiang Lei ◽

Zhenji Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Small Sample Size ◽

Vibration Signal ◽

Rolling Bearing ◽

Small Sample ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Signal Preprocessing ◽

Diagnosis Method

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.

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