Analyzing the Vibration Signals for Bearing Defects Diagnosis Using the Combination of SGWT Feature Extraction and SVM

Document Type

Conference Proceeding

Publication Date

1-1-2018

Volume

51

Issue

24

First Page

221

Keywords

Fault diagnosis, Feature extraction, Second generation wavelet transform (SGWT), Self-organizing maps (SOM), Support Vector Machine (SVM)

Last Page

227

Abstract

The aim of this paper is to introduce a multi-step vibration-based diagnostic algorithm to automatically diagnose bearings faults. The proposed diagnostic scheme extracts the informative features from each component by resorting to the second generation wavelet transform. Undoubtedly, a large dimension of features brought more challenges to detect healthy and defective bearings. In this regard, the dimensionality reduction phase makes use of linear discriminant analysis that aims to obtain a low dimensional representation of high dimensional data as well as achieves maximum separability between different classes. Furthermore, self-organizing maps (SOM) helps in evaluating and facilitating visual comprehension of the extracted features. In the following step, support vector machine (SVM) is used for identifying faulty and fault-free bearings. Finally, the performance of the proposed technique is compared with the previous works.

DOI

10.1016/j.ifacol.2018.09.581

E-ISSN

24058963

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