Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data

Volume: 141, Pages: 106441 - 106441
Published: Jul 1, 2020
Abstract
The fault diagnosis methods of rotating machinery based on machine learning have been developed in the past years, such as support vector machine (SVM) and convolutional neural networks (CNN). SVM just can be only used for the classification of the vector space in which the feature data extracted from raw signals are input data in vector form, so SVM loses its functions while the input feature data are high order tensors which can contain rich...
Paper Details
Title
Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data
Published Date
Jul 1, 2020
Volume
141
Pages
106441 - 106441
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