Analyzing the Vibration Signals for Bearing Defects Diagnosis Using the Combination of SGWT Feature Extraction and SVM
Fault diagnosis, Feature extraction, Second generation wavelet transform (SGWT), Self-organizing maps (SOM), Support Vector Machine (SVM)
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.
Gharesi, Niloofar; Arefi, Mohammad Mehdi; Ebrahimi, Zeinab; Razavi-Far, Roozbeh; Saif, Mehrdad; and Zarei, Jafar. (2018). Analyzing the Vibration Signals for Bearing Defects Diagnosis Using the Combination of SGWT Feature Extraction and SVM.