Support Vector Machines (SVMs)
Support vector machines (SVMs) are one of the most popular machine learning methods used to classify machine health conditions using the selected feature space. In machine fault detection and diagnosis, SVMs are used for learning special patterns from the acquired signal; then these patterns are cla...
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| Vydáno v: | Condition Monitoring with Vibration Signals s. 259 - 277 |
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| Hlavní autoři: | , |
| Médium: | Kapitola |
| Jazyk: | angličtina |
| Vydáno: |
Chichester, UK
Wiley
2019
John Wiley & Sons, Ltd |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9781119544623, 1119544629 |
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| Abstract | Support vector machines (SVMs) are one of the most popular machine learning methods used to classify machine health conditions using the selected feature space. In machine fault detection and diagnosis, SVMs are used for learning special patterns from the acquired signal; then these patterns are classified according to the fault occurrence in the machine. This chapter presents essential concepts of the SVM classifier by giving a brief description of the SVM model for binary classification. Then, it explains the multiclass SVM approach and different techniques that can be used for multiclass SVMs. A considerable amount of literature has been published on the application of SVMs and variants in diagnosing machine faults. Most of these studies introduced pre‐processing techniques that include normalisation, feature extraction, transformation, and feature selection. The data produced during the pre‐processing step represent the final training set that is used as input to SVMs. |
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| AbstractList | Support vector machines (SVMs) are one of the most popular machine learning methods used to classify machine health conditions using the selected feature space. In machine fault detection and diagnosis, SVMs are used for learning special patterns from the acquired signal; then these patterns are classified according to the fault occurrence in the machine. This chapter presents essential concepts of the SVM classifier by giving a brief description of the SVM model for binary classification. Then, it explains the multiclass SVM approach and different techniques that can be used for multiclass SVMs. A considerable amount of literature has been published on the application of SVMs and variants in diagnosing machine faults. Most of these studies introduced pre‐processing techniques that include normalisation, feature extraction, transformation, and feature selection. The data produced during the pre‐processing step represent the final training set that is used as input to SVMs. Support vector machines (SVMs) are one of the most popular machine learning methods used to classify machine health conditions using the selected feature space. In machine fault detection and diagnosis, SVMs are used for learning special patterns from the acquired signal; then these patterns are classified according to the fault occurrence in the machine. This chapter presents essential concepts of the SVM classifier by giving a brief description of the SVM model for binary classification. Then, it explains the multiclass SVM approach and different techniques that can be used for multiclass SVMs. A considerable amount of literature has been published on the application of SVMs and variants in diagnosing machine faults. Most of these studies introduced pre‐processing techniques that include normalisation, feature extraction, transformation, and feature selection. The data produced during the pre‐processing step represent the final training set that is used as input to SVMs. |
| Author | Ahmed, Hosameldin Nandi, Asoke K |
| Author_xml | – sequence: 1 givenname: Hosameldin surname: Ahmed fullname: Ahmed, Hosameldin – sequence: 2 givenname: Asoke K surname: Nandi fullname: Nandi, Asoke K organization: Brunel University London, UK |
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| Copyright | 2019 John Wiley & Sons Ltd 2020 John Wiley & Sons Ltd |
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| DOI | 10.1002/9781119544678.ch13 |
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| Discipline | Engineering |
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| EndPage | 277 |
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| Keywords | Support vector machines Training Condition monitoring Vibrations Rotating machines Machine learning Kernel |
| Language | English |
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| PublicationSubtitle | Compressive Sampling and Learning Algorithms for Rotating Machines |
| PublicationTitle | Condition Monitoring with Vibration Signals |
| PublicationYear | 2019 |
| Publisher | Wiley John Wiley & Sons, Ltd |
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| SubjectTerms | binary classification problem feature extraction feature selection machine fault diagnosis multiclass SVM approach normalisation support vector machines SVM classifier |
| Title | Support Vector Machines (SVMs) |
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