EMCS-SVR: Hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis

Luo, Changqi; Keshtegar, Behrooz; Zhu, Shun‐Peng

doi:10.1016/j.cma.2022.115499

Cited by 69 publications

(23 citation statements)

References 57 publications

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“…Among them, BP neural network, support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN) and decision tree (DT) have been used most frequently (Li et al. , 2022; Luo et al. , 2022a, b; Luo et al.…”

Section: Introductionmentioning

confidence: 99%

“…At present artificial intelligence and machine learning algorithms have gradually penetrated the field of fault diagnosis. Among them, BP neural network, support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN) and decision tree (DT) have been used most frequently (Li et al, 2022;Luo et al, 2022a, b;Luo et al, 2022a, b;Meng et al, 2019). SVM as a classical algorithm is favored by scholars for its unique classification advantages (Garc ıa-Nieto et al, 2015;Li et al, 2020).…”

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confidence: 99%

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A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

Men

et al. 2023

IJSI

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PurposeThis paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.Design/methodology/approachAn intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.FindingsThe fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.Originality/valueIn most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

Men

et al. 2023

IJSI

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“…In reliability assessment, multi-physics coupling, multi-variable, multi-source uncertainty information (Wang et al ., 2022; Liu et al ., 2020; Pan and Deng, 2018; Xue and Deng, 2021) are often involved, so these assessments are even more unacceptable (Liu et al ., 2021b; Gao et al ., 2022; Yang et al ., 2022; Meng et al ., 2022b). Surrogate model (such as Kriging model (Li et al ., 2021; Meng et al ., 2021b), Canonical Low Rank Approximation (CLRA) (Wang et al ., 2019), Polynomial Chaos Expansions (PCE) (Zhu et al ., 2023), Support Vector Regression (SVR) (Luo et al ., 2022a) and Polynomial Chaos-Kriging (PCK) (Meng et al ., 2017; Schöbi et al ., 2017) etc.) is an approximation technique.…”

Section: Introductionmentioning

confidence: 99%

A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

Yang

Meng

Wang

et al. 2023

IJSI

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PurposeThis study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.Design/methodology/approachIn this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.FindingsBy comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.Originality/valueAlthough a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

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“…An et al [ 11 ] combined the GRA and BPNN methods to predict the corrosion of steel bars; the results proved that this method can predict well. Luo et al [ 12 , 13 ] developed a hybrid enhanced Monte Carlo simulation and a dynamical adaptive enhanced simulation method coupled with support vector regression, which showed strong capability for application in the fatigue assessment of turbine bladed disks and structural reliability. Muiga et al [ 14 ] adopted a gray prediction model (GM (1, 1)) to evaluate the carbonization of long-span reinforced concrete bridges.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models

Liu

Xue-wei

2023

Computational Intelligence and Neuroscience

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The corrosion of steel bars in concrete has a significant impact on the durability of constructed structures. Based on the gray relational analysis (GRA) of the accelerated corrosion data and practical engineering data using MATLAB, a back propagation neural network (BPNN) model, a multivariable gray prediction model (GM (1, N)), and an optimization multivariable gray prediction model (OGM (1, N)) of steel corrosion were established by using a sequence of the key affecting factors. By comparing the prediction results of the three models, it is found that the GM (1, N) model has larger fitting and prediction errors for steel corrosion, while the OGM (1, N) model has smaller prediction errors in the accelerated corrosion data; the BPNN model offers more accurate predictions of the practical engineering data. The results show that the BPNN and OGM (1, N) models are all suitable for the prediction of steel bar corrosion in concrete structures.

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EMCS-SVR: Hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis

Cited by 69 publications

References 57 publications

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

A hybrid intelligent gearbox fault diagnosis method based on EWCEEMD and whale optimization algorithm-optimized SVM

A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models

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