scholarly journals Fault Diagnosis of Bearings Based on KJADE and VNWOA-LSSVM Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Tao Wu ◽  
Chang Chun Liu ◽  
Cheng He
Keyword(s):  
Fault Diagnosis ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Support Vector ◽  
Machine Method ◽  
Von Neumann ◽  
Whale Optimization ◽  
Nonlinear Features ◽  
Low Dimensional

In order to accurately diagnose the faulty parts of the rolling bearing under different operating conditions, the KJADE (Kernel Function Joint Approximate Diagonalization of Eigenmatrices) algorithm is proposed to reduce the dimensionality of the high-dimensional feature data. Then, the VNWOA (Von Neumann Topology Whale Optimization Algorithm) is used to optimize the LSSVM (Least Squares Support Vector Machine) method to diagnose the fault type of the rolling bearing. The VNWOA algorithm is used to optimize the regularization parameters and kernel parameters of LSSVM. The low-dimensional nonlinear features contained in the multidomain feature set are extracted by KJADE and compared with the results of PCA, LDA, KPCA, and JADE methods. Finally, VNWOA-LSSVM is used to identify bearing faults and compare them with LSSVM, GA-LSSVM, PSO-LSSVM, and WOA-LSSVM. The results show that the recognition rate of fault diagnosis is up to 98.67% by using VNWOA-LSSVM. The method based on KJADE and VNWOA-LSSVM can diagnose and identify fault signals more effectively and has higher classification accuracy.

scholarly journals Rolling bearing fault diagnosis based on MEEMD sample entropy and SSA-SVM

2021 ◽  
Vol 2125 (1) ◽  
pp. 012003
Author(s):  
Xuguang Li ◽  
Liyou Fu
Keyword(s):  
Fault Diagnosis ◽  
Search Algorithm ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Kernel Parameter

Abstract The penalty parameter (c) and kernel parameter (g) contained in Support Vector Machine (SVM) cannot be adaptively selected according to actual samples, which results in low classification accuracy and slow convergence speed. A novel sparrow search algorithm was used to optimize the parameters of SVM classifier. Firstly, an improved ensemble empirical mode decomposition (MEEMD) method was used to decompose non-stationary and nonlinear vibration signals, and the eigenmode function (IMF) was obtained by removing abnormal signals from the original signals through permutation entropy, and the sample entropy was extracted. Finally, a fault diagnosis model based on SSA-SVM is constructed, and the high recognition rate and effectiveness of this method are proved by simulation and experimental data analysis.

scholarly journals Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Min Zhang ◽  
Zhenyu Cai ◽  
Wenming Cheng
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Principal Component ◽  
Rolling Bearing ◽  
Total Sample ◽  
Parameters Optimization ◽  
Support Vector ◽  
Significant Information ◽  
Multiple Faults

Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) with particle parameter adaptive (PPA) is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE_PPA_MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

The Application of PCA and SVM in Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 430-432 ◽  
pp. 1163-1166 ◽  
Author(s):  
Meng Li
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Support Vector ◽  
Fault Pattern ◽  
Feature Extracting ◽  
Bearing Fault Diagnosis ◽  
Independent Eigenvector ◽  
Correlated Variable ◽  
Low Dimensional

The key to the fault diagnosis is feature extracting and fault pattern classifying. Principal components analysis (PCA) and support vector machine (SVM) method are introduced to recognize the fault pattern of the rolling bearing in this paper. Multidimensional correlated variable is converted into low dimensional independent eigenvector by means of PCA. The pattern recognition and the nonlinear regression are achieved by the method of SVM. In the light of the feature of vibrating signals, eigenvector is obtained using PCA, fault diagnosis of rolling bearing is recognized correspondingly using SVM fault classifier. Theory and experiment show that the recognition of fault diagnosis of rolling bearing based on PCA and SVM theory is available in the fault pattern recognition and provides a new approach to intelligent fault diagnosis.

scholarly journals Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 292 ◽  
Author(s):  
Qingyun Liu ◽  
Haiyang Pan ◽  
Jinde Zheng ◽  
Jinyu Tong ◽  
Jiahan Bao
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Spline Interpolation ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Fuzzy Entropy ◽  
Coarse Grained ◽  
Support Vector ◽  
Nonlinear Method ◽  
Diagnosis Method

Multiscale fuzzy entropy (MFE), as an enhanced multiscale sample entropy (MSE) method, is an effective nonlinear method for measuring the complexity of time series. In this paper, an improved MFE algorithm termed composite interpolation-based multiscale fuzzy entropy (CIMFE) is proposed by using cubic spline interpolation of the time series over different scales to overcome the drawbacks of the coarse-grained MFE process. The proposed CIMFE method is compared with MSE and MFE by analyzing simulation signals and the result indicates that CIMFE is more robust than MSE and MFE in analyzing short time series. Taking this into account, a new fault diagnosis method for rolling bearing is presented by combining CIMFE for feature extraction with Laplacian support vector machine for fault feature classification. Finally, the proposed fault diagnosis method is applied to the experiment data of rolling bearing by comparing with the MSE, MFE and other existing methods, and the recognition rate of the proposed method is 98.71%, 98.71%, 98.71%, 98.71% and 100% under different training samples (5, 10, 15, 20 and 25), which is higher than that of the existing methods.

scholarly journals Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 621 ◽  
Author(s):  
Zhilin Dong ◽  
Jinde Zheng ◽  
Siqi Huang ◽  
Haiyang Pan ◽  
Qingyun Liu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Coarse Grained ◽  
Fault Classification ◽  
Time Shift ◽  
Permutation Entropy ◽  
Support Vector ◽  
Multi Scale

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.

Multiscale morphological manifold for rolling bearing fault diagnosis

2016 ◽  
Vol 231 (19) ◽  
pp. 3516-3529 ◽  
Author(s):  
Yi Feng ◽  
Baochun Lu ◽  
Dengfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Rolling Bearing ◽  
Morphological Transformation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Nonlinear Features ◽  
Signal Sample ◽  
Difference Filter ◽  
Low Dimensional

The vibration signals of fault rolling bearing are high-dimensional information with complex components. In order to identify different classes of bearing fault, a new multiscale morphological manifold method based on multiscale morphology and manifold learning is proposed. The multiscale morphological manifold method consists of three main steps. Firstly, multiscale difference filter based on multiscale morphological transformation is applied to obtain multiscale observation results of each signal sample. Secondly, the nonlinear feature vectors of each signal sample are constructed according to the observation approach. Finally, manifold learning is introduced to extract the low-dimensional multiscale morphological manifold features through reducing the dimension of nonlinear features. The low-dimensional multiscale morphological manifold features can reveal the differences of signal classes, which are applicable for fault diagnosis. The performance of proposed method is tested by experimental data from bearings with different types of defects. Experimental verifications confirm that the proposed method is applicable and effective for rolling bearing fault diagnosis.

scholarly journals A Fault Diagnosis Method of Rolling Bearing Integrated with Cooperative Energy Feature Extraction and Improved Least-Squares Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Xu ◽  
Darong Huang ◽  
Tang Min ◽  
Yunhui Ou
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Support Vector ◽  
Swarm Optimization ◽  
Energy Characteristics ◽  
Wavelet Energy ◽  
Energy Feature

To solve the problem that the bearing fault of variable working conditions is challenging to identify and classify in the industrial field, this paper proposes a new method based on optimization of multidimension fault energy characteristics and integrates with an improved least-squares support vector machine (LSSVM). First, because the traditional wavelet energy feature is difficult to effectively reflect the characteristics of rolling bearing under different working conditions, based on analyzing the wavelet energy feature extraction in detail, a collaborative method of multidimension fault energy feature extraction combined with the method of Transfer Component Analysis (TCA) is constructed, which improves the discrimination between the different features and the compactness between the same features of rolling bearing faults. Then, for solving the problem of the local optimal of particle swarm optimization (PSO) in fault diagnosis and recognition of rolling bearing, an improved LSSVM based on particle swarm optimization and wavelet mutation optimization is established to realize the collaborative optimization and adjustment of LSSVM dynamic parameters. Based on the improved LSSVM and optimization of multidimensional energy characteristics, a new method for fault diagnosis of rolling bearing is designed. Finally, the simulation and analysis of the proposed algorithm are verified by the experimental data of different working conditions. The experimental results show that this method can effectively extract the multidimensional fault characteristics under variable working conditions and has a high fault recognition rate.

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