Non-parallel bounded support matrix machine and its application in roller bearing fault diagnosis

•A novel non-parallel bounded support matrix machine (NPBSMM) is proposed.•A constraint norm group (CNG) is constructed, which can suppress negative influence of outliers and enhance robustness.•The dual problem of NPBSMM avoids the calculation of matrix inversion.•Multi-rank left and right projecti...

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Bibliographic Details
Published in:Information sciences Vol. 624; pp. 395 - 415
Main Authors: Pan, Haiyang, Xu, Haifeng, Zheng, Jinde, Tong, Jinyu
Format: Journal Article
Language:English
Published: Elsevier Inc 01.05.2023
Subjects:
Constraint norm group
Fault diagnosis
Multi-rank left and right projection matrix
Non-parallel bounded support matrix machine
Roller bearing
Fault diagnosis
Roller bearing
Non-parallel bounded support matrix machine
Multi-rank left and right projection matrix
Constraint norm group
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•A novel non-parallel bounded support matrix machine (NPBSMM) is proposed.•A constraint norm group (CNG) is constructed, which can suppress negative influence of outliers and enhance robustness.•The dual problem of NPBSMM avoids the calculation of matrix inversion.•Multi-rank left and right projection matrices are employed to realize a better ability of data fitting. At present, the excellent performance of support vector machine (SVM) has made it successfully applied in many fields. However, when SVM is used for two-dimensional matrix data classification, vectorization of these data easily leads to dimension curse and the loss of structural information. Moreover, SVM is sensitive to outliers, which causes the hyperplane to move towards outliers. Therefore, this paper proposes a novel classification method for data in matrix-form, named non-parallel bounded support matrix machine (NPBSMM). In NPBSMM, a constraint norm group (CNG) is constructed and applied to objective function, which can not only suppress the negative impact of outliers on the model, but also make NPBSMM has better sparsity. By constructing CNG, the operation of matrix inversion in dual problem of traditional classification methods is avoided, so NPBSMM is more suitable for solving large-scale data problems. Further, to extract structure information of matrix for modeling, multi-rank left and right projection matrices are employed to establish objective function, which makes NPBSMM has a better ability of data fitting. Experiments performed on three roller bearing fault datasets show that the proposed NPBSMM method has powerful performance and robustness as compared with other typical classification methods.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.12.090