A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Li, Yunqian; Huang, Darong; Qin, Zixia

doi:10.1155/2021/6627367

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…Here, linear equation method remains the solution and it decreases the complexity of this method, with an increase in revolving speed. These benefits make it different from other enhancements made in SVM method [18]. The fundamentals of this technique are defined in this section.…”

Section: Das Classification Using Bso-lssvm Modelmentioning

confidence: 99%

Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Al‐Wesabi¹,

Alsolai²,

Hilal³

et al. 2022

Computers, Materials &Amp; Continua

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Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear; with tremendously-limiting measures of social distancing and lockdown in force; and with high rates of new cases and mortalities. With this motivation, the current study aims at investigating the DAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population. The current study proposes to develop Intelligent Feature Subset Selection with Machine Learning-based DAS predictive (IFSSML-DAS) model. The presented IFSSML-DAS model involves data preprocessing, Feature Subset Selection (FSS), classification, and parameter tuning. Besides, IFSSML-DAS model uses Group Gray Wolf Optimization based FSS (GGWO-FSS) technique to reduce the curse of dimensionality. In addition, Beetle Swarm Optimization based Least Square Support Vector Machine (BSO-LSSVM) model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm. The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures. The outcome of the

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Section: Das Classification Using Bso-lssvm Modelmentioning

confidence: 99%

Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Al‐Wesabi¹,

Alsolai²,

Hilal³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Quantitative methods for memory test of time series include R/S test [21], modified R/S test [22], KPSS test [23], and LM test [24].…”

Section: Nonlinear Cointegration Test and Necm Modelmentioning

confidence: 99%

“…e neural network algorithm and wavelet neural network shall be prone to overfitting phenomenon, and the generalization ability of the model is not good as well [21], including relational testing and modeling have poor applicability. In view of this, this article proposes to apply the LS-SVM [22] with excellent learning performance and strong generalization ability to test the nonlinear cointegration relationship among small sample time series. Meanwhile, the PSO, which is widely used in function optimization, neural network training, fuzzy system control, and other genetic algorithms, is selected to jointly optimize the parameters of LS-SVM.…”

Section: Introductionmentioning

confidence: 99%

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

2022

Complexity

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According to the definition of nonlinear cointegration, this article studies the small sample nonlinear cointegration test and NECM (Nonlinear Error Correction Model) based on the LS-SVM (Least Squares Support Vector Machine) optimized by PSO (Particle Swarm Optimization). And the logical process of this method is also designed. Then, we carry out empirical research on the ship maintenance cost index and the several price indexes. Based on the judgment of the type of cointegration test relationship, the test of the nonlinear cointegration test relationship of small samples is realized, and the NECM for predicting the ship maintenance cost index is established as well, which is compared with the VAR model of linear vector. The research result shows that the small sample nonlinear cointegration test and modeling method based on the LS-SVM Optimized by PSO can describe the nonlinear cointegration test relationship of the small sample system. And the NECM has better performance. The prediction effect can effectively predict small sample nonlinear systems. We also compare the prediction results with the wavelet neural network algorithm, and the results show that the generalization ability of LS-SVM Optimized by PSO is better, and the prediction accuracy of small samples is higher.

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“…However, SVM is complicated to solve the non-equation constraint problem, and in order to reduce the solution difficulty, Suykens [ 24 ] improved SVM and proposed the least square support vector machine (LSSVM), which replaced the non-equation constraint in SVM with an equation constraint, greatly reducing the solution difficulty. The LSSVM algorithm has been widely applied in the field of industrial intelligence in recent years [ 25 , 26 , 27 , 28 ]. He et al [ 29 ] used wavelet packet transform to extract fault features and combined them with LSSVM to complete the fault identification of circuit output voltage signals.…”

Section: Introductionmentioning

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.

show abstract

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Cited by 8 publications

References 33 publications

Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

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

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