Performance Improvement of Feature-Based Fault Classification for Rotor System

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

“…Table 3 lists the experimental results. Under Normal, Rubbing, Unbalance, and Misalignment conditions, the proposed methods showed better performance than the existing methods [3,5]. In this study, an attempt was made to improve performance through k-fold cross-validation, but the following problems were encountered.…”

“…At this time, to demonstrate the superiority of the methods used in this experiment, they were compared with one of the most commonly used methods, the method of applying SVM (Support Vector Machine), after feature selection based on the GA (genetic algorithm) after extracting the hand-crafted features from a raw signal and [3]. The [3] method is a method of applying SVM by mixing a GA and PCA (Principal Component Analysis) from a list of hand-crafted feature values through feature engineering. The proposed methods were also compared with the MLP (Multi-Layer Perceptron) method [5] from the same feature engineering [3] to determine if it shows better performance in training after data visualization.…”

“…The [3] method is a method of applying SVM by mixing a GA and PCA (Principal Component Analysis) from a list of hand-crafted feature values through feature engineering. The proposed methods were also compared with the MLP (Multi-Layer Perceptron) method [5] from the same feature engineering [3] to determine if it shows better performance in training after data visualization. Table 3 lists the experimental results.…”

“…The amplification of small vibrations generated in the rotating part of the gravity acceleration equipment may result in damage to the shafts rotating at high speeds, which may lead to serious accidents. Traditional machine learning (ML) that uses feature-based methods [3][4][5] on hand-crafted lists of feature engineering has limitations that cannot improve performance. Furthermore, it is difficult to say that human-designed features are for defective representation.…”

“…Fault Detection using ML: Many studies on vibration-related failures and predictive failure diagnosis have been conducted [3,[9][10][11][12][13][14][15][16][17][18][19][20][21]. Lee et al [3] proposed a rotating mechanism system-a mixture of feature extraction and selection classifies it as a Support Vector Machine (SVM) [4]. Zhang et al [5] proposed fault detection for bearing wind turbines using ANNs (Artificial Neural Networks).…”