Optical fiber intrusion signal unmixing by constrained quadratic programming approach

Zhang, Yuan; Tian, Kaixin; Pang, Liping; Qu, Hongquan; Tian, Qing; Zhu, Han

doi:10.1016/j.ijleo.2018.07.060

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…C is the penalty factor, balancing the maximum interval and the minimum number of misclassification points. Since formula (1) is a convex quadratic programming problem [18], solving the Lagrange multiplier can obtain the decision function…”

Section: Introduction To Svmsmentioning

confidence: 99%

SVM bearing fault diagnosis based on fast inter-class distance in the feature space and DMPSO algorithm

Song,

Yu,

Yang

et al. 2024

Meas. Sci. Technol.

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Support Vector Machines (SVM) have good processing performance for small sample datasets. The giant search space for kernel parameters and the tendency of parameter optimization to fall into local optima are two essential factors that affect the generalization ability of SVM models and, thus, affect the accuracy of fault diagnosis results. Propose using Fast Inter-Class Distance (FICDF) in the feature space to reduce the search space for kernel function parameters and then use Differential Mutation Particle Swarm Optimization (DMPSO) to optimize kernel function parameters to improve the generalization ability and classification accuracy of the SVM model. Firstly, the FICDF algorithm is used to calculate the Euclidean distance between classes, and a fast segmentation idea is proposed for fast operations to obtain a smaller kernel parameter search space. Then, the global search ability of the differential mutation Particle swarm optimization algorithm is used to obtain the optimal parameter combination of the support vector machine model. Finally, the fault diagnosis model of the support vector machine is applied to the fault diagnosis of rolling bearings. The experimental results show that compared with other fault diagnosis methods, this model method has higher classification accuracy and verifies its better classification speed.

show abstract

Section: Introduction To Svmsmentioning

confidence: 99%

SVM bearing fault diagnosis based on fast inter-class distance in the feature space and DMPSO algorithm

Song,

Yu,

Yang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, the SVM needs to settle the quadratic programming problem when looking for the separating hyperplane [11], and it is easily affected by the distribution of the support vector, hence the classification accuracy is greatly reduced in the case of a heavily large amount of data. Due to the fact that the support vectors [5,[12][13][14][15] are usually located in the outer contour area of the sample set, related scholars proposed the convex hull algorithm [16][17][18] to quickly obtain the support vectors.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine fault diagnosis based on sparse scaling convex hull

Renwang¹,

Baiqian²,

Lei³

et al. 2022

Meas. Sci. Technol.

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To solve the problem that the convex hull coverage model of the dataset cannot reflect the effective distribution of the samples and the feature dimension of the sample points is too high in the process of fault diagnosis. A Sparse Scaled Convex Hull (SSCH) based support vector machine classification method is proposed and applied to fault diagnosis of roller bearings. The dimensionality reduction of the features of the sample set is carried out by random forest (RF). Firstly, the optimized sample subsets are obtained by sparse approximation, and the scale factor of the convex hull of the optimized sample set is adjusted so that the convex hulls of various sample sets are linearly separable. Secondly, to solve the problem of too high feature dimension, the importance of features is evaluated by random forest, and some redundant features are removed. Finally, the support vector machine model is constructed by the closest points between the convex hulls. The experimental results and related theories show that the proposed method has high classification accuracy in bearing fault diagnosis，the method can reliably identify different types of faults in rolling bearings.

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Optical fiber intrusion signal unmixing by constrained quadratic programming approach

Cited by 2 publications

References 11 publications

SVM bearing fault diagnosis based on fast inter-class distance in the feature space and DMPSO algorithm

SVM bearing fault diagnosis based on fast inter-class distance in the feature space and DMPSO algorithm

Support vector machine fault diagnosis based on sparse scaling convex hull

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