A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. (May 2020) - Kuncan, Melih; Kaplan, Kaplan; Mi̇naz, Mehmet Recep; Kaya, Yılmaz; Ertunç, H. Metin
Abstract: Bearing is one of the most critical parts used in rotary machines. Bearing faults break down the mechanism where it is located. Moreover, the faults may cause to malfunction by spreading to the entire system. Thus this may result in catastrophic failure eventually. Precise and decisive feature extraction from the raw vibration signal maintains to be one of the current topics explored for fault diagnosis in bearings. In this study, vibration signals are obtained from bearings which are formed with artificial faults of specific dimensions from a bearing test setup. Instead of employing traditional feature extraction methods found in the literature, a novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied. The proposed approach is a statistical method that uses patterns obtained from comparisons between neighbors of each value on vibration signals. The study aims to identify the size (mm) of the fault by determining the bearing part (inner ring, outer ring, ball) from which the faults in the bearings are caused. Several classification techniques were performed by using ternary patterns with RF (Random Forest), k-NN (k-nearest neighbor), SVM (Support Vector Machine), BayesNet, ANN (Artificial Neural Networks) models. As a result of analyzing the signals obtained from the experimental setup with the proposed model, 91.25% for dataset_1 (different speed), 100% for dataset_2 (fault type — inner ring, outer ring, ball) and 100% for dataset_3 (fault size (mm)) success rates are determined. Highlights: A novel approach is proposed to extraction of features from bearing signals. One advantage is that this method uses all data points for feature extraction. It is fast and can be use in real-time application. High accuracies achieved for bearing fault classification. Original data (Experimental setup of authors).