The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Peng Chen - One of the best experts on this subject based on the ideXlab platform.
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A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine
IEEE Access, 2019Co-Authors: Ke Li, Rui Zhang, Fucai Li, Lei Su, Huaqing Wang, Peng ChenAbstract:In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support Vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support Vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the “feature extraction formula” is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support Vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal Linearly Independent Vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance.
Ke Li - One of the best experts on this subject based on the ideXlab platform.
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A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine
IEEE Access, 2019Co-Authors: Ke Li, Rui Zhang, Fucai Li, Lei Su, Huaqing Wang, Peng ChenAbstract:In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support Vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support Vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the “feature extraction formula” is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support Vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal Linearly Independent Vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance.
Ferdinando A. Mussa-ivaldi - One of the best experts on this subject based on the ideXlab platform.
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From basis functions to basis fields: Vector field approximation from sparse data
Biological Cybernetics, 1992Co-Authors: Ferdinando A. Mussa-ivaldiAbstract:Recent investigations (Poggio and Girosi 1990b) have pointed out the equivalence between a wide class of learning problems and the reconstruction of a real-valued function from a sparse set of data. However, in order to process sensory information and to generate purposeful actions living organisms must deal not only with real-valued functions but also with Vector-valued mappings. Examples of such Vector-valued mappings range from the optical flow fields associated with visual motion to the fields of mechanical forces produced by neuromuscular activation. In this paper, I discuss the issue of Vector-field processing from a broad computational perspective. A variety of Vector patterns can be efficiently represented by a combination of Linearly Independent Vector fields that I call “basis fields”. Basis fields offer in some cases a better alternative to treating each component of a Vector as an Independent scalar entity. In spite of its apparent simplicity, such a component-based representation is bound to change with any change of coordinates. In contrast, Vector-valued primitives such as basis fields generate Vector field representations that are invariant under coordinate transformations.
Rui Zhang - One of the best experts on this subject based on the ideXlab platform.
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A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine
IEEE Access, 2019Co-Authors: Ke Li, Rui Zhang, Fucai Li, Lei Su, Huaqing Wang, Peng ChenAbstract:In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support Vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support Vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the “feature extraction formula” is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support Vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal Linearly Independent Vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance.
Fucai Li - One of the best experts on this subject based on the ideXlab platform.
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A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine
IEEE Access, 2019Co-Authors: Ke Li, Rui Zhang, Fucai Li, Lei Su, Huaqing Wang, Peng ChenAbstract:In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support Vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support Vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the “feature extraction formula” is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support Vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal Linearly Independent Vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance.