Application of Hybrid Model between the Technique for Order of Preference by Similarity to Ideal Solution and Feature Extractions for Bearing Defect Classification

Abstract

This paper describes a development that offers new opportunities for detecting faulty bearings. Prioritization is based on the technique for order of preference by similarity to the ideal solution (TOPSIS) for the most discriminative features in the faulty bearing dataset. The proposed model is divided into three steps: feature extraction, feature selection, and classification. In feature extraction, variational mode decomposition (VMD) and fast Fourier transform (FFT) are used to extract features from the measured signal of the test motors and use the symmetrical uncertainty (SU) value for calculation, reducing the redundancy of data. In terms of feature selection, the TOPSIS method is used instead of the traditional filtering method, which is applied to analysis and decision making, and important features are selected from seven filtering methods. Finally, in order to validate the classification ability of the proposed model, k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural networks (ANN) are used as independent classifiers. The effectiveness of the proposed model is evaluated by applying two bearing datasets, namely the current dataset of motor vibration signals and the dataset of bearing motors provided by Case Western Reserve University (CWRU). The results show that the comparison of the proposed model with other models shows the feasibility of this study.