scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang-qiang Chen ◽  
Shao-wu Dai ◽  
Hong-de Dai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Main Fault

The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

scholarly journals Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 762
Author(s):  
Maoyou Ye ◽  
Xiaoan Yan ◽  
Minping Jia
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Permutation Entropy ◽  
Support Vector ◽  
Detection Accuracy ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Bearing Vibration

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

An optimized variational mode decomposition method and its application in vibration signal analysis of bearings

2021 ◽  
pp. 147592172110574
Author(s):  
Jun Gu ◽  
Yuxing Peng ◽  
Hao Lu ◽  
Xiangdong Chang ◽  
Shuang Cao ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Input Parameter ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Penalty Factor

The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.

scholarly journals Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 519 ◽  
Author(s):  
Weibo Zhang ◽  
Jianzhong Zhou
Keyword(s):  
Diagnostic Algorithm ◽  
Feature Space ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

scholarly journals A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 995 ◽  
Author(s):  
Tao Liang ◽  
Hao Lu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Mode Decomposition

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

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

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.

scholarly journals Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 503
Author(s):  
Dongri Xie ◽  
Shaohua Hong ◽  
Chaojun Yao
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Marine Animals ◽  
Practical Applications ◽  
Radiated Noise ◽  
Mode Decomposition

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 290
Author(s):  
Jianghua Ge ◽  
Tianyu Niu ◽  
Di Xu ◽  
Guibin Yin ◽  
Yaping Wang
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Signal Reconstruction ◽  
Pearson Correlation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
Reconstructed Signal

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.

Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine

2016 ◽  
Vol 39 (7) ◽  
pp. 1000-1006 ◽  
Author(s):  
Xueli An ◽  
Yongjun Tang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Wind Turbine ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition

For the unsteady characteristics of a fault vibration signal of a wind turbine’s rolling bearing, a bearing fault diagnosis method based on variational mode decomposition of the energy distribution is proposed. Firstly, variational mode decomposition is used to decompose the original vibration signal into a finite number of stationary components. Then, some components which comprise the major fault information are selected for further analysis. When a rolling bearing fault occurs, the energy in different frequency bands of the vibration acceleration signals will change. Energy characteristic parameters can then be extracted from each component as the input parameters of the classifier, based on the K nearest neighbour algorithm. This can identify the type of fault in the rolling bearing. The vibration signals from a spherical roller bearing in its normal state, with an outer race fault, with an inner race fault and with a roller fault were analyzed. The results showed that the proposed method (variational mode decomposition is used as a pre-processor to extract the energy of each frequency band as the characteristic parameter) can identify the working state and fault type of rolling bearings in a wind turbine.

Sign in / Sign up

Export Citation Format

Share Document