Desenvolvimento de sistema de diagnóstico de falhas em roletes de transportadores de correia

Abstract

Belt conveyors are essential equipment in mining industry and require constant monitoring to maintain good reliability. In order to support the belt and the material being conveyed, rollers are components that constantly fail during operation, in which they present faults in bearings and surface wear in the shell as the most common failure modes. Thus, monitoring based on predictive maintenance is essential, and machine learning techniques can be used as an alternative for detecting equipment failures. In diagnostics using machine learning, the feature selection step is important to avoid loss of accuracy in the classification of the equipment's condition. The present study analyzes the performance of the decision tree algorithm and Analysis of Variance (ANOVA) as alternative methods for dimensionality reduction. Initially, the vibration signals were collected on the rollers of a belt conveyor bench and the Wavelet Packet Decomposition (WPD) was applied to the signals to obtain the energy ranges, which were used as features for classification. After the determination of the best features, two approaches were analyzed for the selection of features: one with the application of the method without dimensionality reduction and the other with the application of the decision tree. In addition, different classification algorithms were used: Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN). As a result, it was found a superior performance of diagnostic accuracy in all techniques with a reduction in the dimensionality of the characteristics selected by the decision tree. In addition, SVM, kNN and ANN showed increases in accuracy ranging among the fault diagnosis models approached.