Support vector machine fault diagnosis based on sparse scaling convex hull

Renwang, Song; Baiqian, Yu; Lei, Yang; Shi, Hui; Dong, Zengshou

doi:10.1088/1361-6501/aca217

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…In addition, to validate the more superior performance of the proposed method, six other approaches are also employed for rolling bearing fault diagnosis, including the EMD-CSvDE, VMD-CSvDE, SoVMD-multiscale sample entropy (MSE) [ 44 ], SoVMD-multiscale permutation entropy (MPE) [ 45 ], support vector machine (SVM) [ 46 ], and artificial neural network (ANN) [ 47 ]. More specifically, the first four comparison methods are designed to analyze the contribution of each link in the process of fault diagnosis, i.e., SoVMD-based multiscale frequency component extraction and CSvDE-based MsFFS construction.…”

Section: Experiments Validation and Results Discussionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

Jiang

Shan

Xue

et al. 2023

Entropy

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Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.

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Section: Experiments Validation and Results Discussionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

Jiang

Shan

Xue

et al. 2023

Entropy

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show abstract

“…Therefore, machine learning-based methods for bearing multiclass fault diagnosis have become a hot research topic in the industry. However, traditional machine learning methods such as artificial neural networks [6], random forests [7], and support vector machines [8] learn only shallow features for bearing fault characteristics [9], which are not suitable for highdimensional data.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks

Li,

Zhao

2024

Meas. Sci. Technol.

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Insufficient and imbalanced samples pose a significant challenge in bearing fault diagnosis, leading to low diagnosis accuracy. However, the fault characteristics of vibration signals are weak and difficult to extract when faults occur in the early stage. This paper proposes an effective fault diagnosis method that addresses small and imbalanced sample problems under noise interference. First, the number of faulty samples in the form of 1D signals is increased mainly by the sliding split sampling method. The preprocessed data are used to create 2D time–frequency diagrams using the continuous wavelet transform (CWT), which can extract effective features to improve the data quality. Subsequently, the minority samples are oversampled by combining Synthetic Minority Oversampling Technique (SMOTE) to realize TFCAO. Moreover, the clustering method and random undersampling method are introduced to prevent the overfitting and underfitting problems respectively. Then, we propose a hybrid attention mechanism to enhance the extraction of effective feature information. This combination, integrating CWT with a multicolumn modified DRN, effectively extracts fault characteristics and suppresses noise effects. The experimental results demonstrate the effectiveness of the proposed method by comparison with other advanced methods using two case studies of bearing datasets.

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An axiomatic fuzzy set theory-based fault diagnosis approach for rolling bearings

Wang,

Liu,

Zhai

et al. 2024

Engineering Applications of Artificial Intelligence

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Support vector machine fault diagnosis based on sparse scaling convex hull

Cited by 8 publications

References 34 publications

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks

An axiomatic fuzzy set theory-based fault diagnosis approach for rolling bearings

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