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

机译：使用SGWT特征提取和SVM的组合分析用于轴承缺陷诊断的振动信号

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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.
机译：本文的目的是引入一种基于多步振动的诊断算法来自动诊断轴承故障。所提出的诊断方案通过诉诸第二代小波变换来提取每个组件的信息特征。毫无疑问，一个巨大的特征维度带来了更多挑战来检测健康和有缺陷的轴承。在这方面，维度降低阶段利用线性判别分析，该分析旨在获得高维数据的低尺寸表示，并实现不同类之间的最大可分离性。此外，自组织地图（SOM）有助于评估和促进提取特征的视觉理解。在以下步骤中，支持向量机（SVM）用于识别故障和无故障轴承。最后，将所提出的技术的性能与以前的作品进行比较。

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