A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings

An, Guoqing; Tong, Qingbin; Zhang, Yanan; Liu, Ruifang; Li, Weili; Cao, Junci; Lin, Yi; Wang, Qiang; Zhu, Ying; Xiao-wen, PU

doi:10.1155/2021/6629474

Cited by 7 publications

(9 citation statements)

References 31 publications

(32 reference statements)

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“…erefore, these two moment invariants are selected. e seven invariant moments are given in the following formula [16]:…”

Section: Two Momentmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests.

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“…erefore, these two moment invariants are selected. e seven invariant moments are given in the following formula [16]:…”

Section: Two Momentmentioning

confidence: 99%

Intelligent Storage Data Classification System Based on the BP Neural Network

Li¹

2022

Journal of Control Science and Engineering

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“…VMD is an adaptive signal decomposition method that uses a non-recursive decomposition mode to decompose the signal into a specified number of IMFs with different center frequencies according to a predetermined number of modes K and a penalty factor α . It gets rid of the uncertainty of the number of IMFs caused by the traditional method of EMD decomposition as well as the end effect and modal mixing problems encountered and can better highlight the characteristic information of the signal [ 33 ]. The expression of the k -th order eigenmode function is obtained by VMD decomposition, that is:

where

is the instantaneous amplitude of

.…”

Section: Feature Extractionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Jin

Chen

Yang

2022

Entropy

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In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.

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“…wavelet transform (WT), empirical mode decomposition (EMD) and local mean decomposition (LMD)) in fault diagnosis. However, VMD needs to manually set the parameters before signal decomposition, in which the decomposed mode number K and the penalty factor α have a great impact on the decomposition results [4], so it is necessary to introduce an effective method to automatically determine the optimal decomposition parameters. Currently, two familiar techniques (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…the appearing of the undefined value, the ignoring of amplitude differences), their feature extraction performance is greatly limited in strong noise scenario. (4) In the fault identification of traditional fault diagnosis methods, several classification models (i.e. ANN, KNN, SVM and LSSVM) are often adopted for identifying bearing fault types, and their model parameters are determined by some traditional optimizers (e.g.…”

Section: Introductionmentioning

confidence: 99%

A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions

Yan,

Hua,

Jiang

et al. 2024

Meas. Sci. Technol.

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Aiming at the problem that the fault information of rolling bearings under harsh operation environment is easily submerged by strong noise interference, which causes the traditional method to be difficult to identify bearing faults effectively, this paper proposes a novel robust intelligent fault diagnosis method for rolling bearings based on sparsity-assisted parameter adjustable variational mode decomposition (SPAVMD) and whale optimization algorithm-based optimized least-squares support vector machine (WOA-LSSVM). Firstly, a sparsity measurement named the improved Gini index (IGINI) is introduced as the fitness function of grid search algorithm (GSA) to adaptively adjust and search for the optimal decomposed mode number K and penalty factor α of VMD. Additionally, VMD containing the optimal parameters is adopted to decompose the original bearing vibration signal into several intrinsic mode function (IMF), and the effective signal reconstruction is performed by screening the sensitive IMF components according to the effective weighted kurtosis Gini index criterion (EWKGI). Subsequently, the refined composite multiscale dispersion entropy (RCMDE) of the reconstructed signal is further calculated to establish a multi-dimensional feature vector set. Finally, the constructed feature vector set is fed into the whale optimization algorithm-based optimized least squares support vector machine (WOA-LSSVM) to achieve automatic fault identification of rolling bearings. The effectiveness of the proposed method is verified by two experimental examples. Experimental results show that the proposed method has higher fault recognition accuracy and better robustness against noise than other homologous methods in noisy conditions. This study provides a new perspective for the developing of robust diagnosis methods.

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A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings

Cited by 7 publications

References 31 publications

Intelligent Storage Data Classification System Based on the BP Neural Network

Intelligent Storage Data Classification System Based on the BP Neural Network

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions

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