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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Bibliographic Details
Published in:Condition Monitoring with Vibration Signals pp. 259 - 277
Main Authors: Ahmed, Hosameldin, Nandi, Asoke K
Format: Book Chapter
Language:English
Published: Chichester, UK Wiley 2019
John Wiley & Sons, Ltd
Edition:1
Subjects:
binary classification problem
feature extraction
feature selection
machine fault diagnosis
multiclass SVM approach
normalisation
support vector machines
SVM classifier
Support vector machines
Training
Condition monitoring
Vibrations
Rotating machines
Machine learning
Kernel
ISBN:9781119544623, 1119544629
Online Access:Get full text
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Summary: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.
ISBN:9781119544623
1119544629
DOI:10.1002/9781119544678.ch13