Eventos Anais de eventos
COBEM 2023
27th International Congress of Mechanical Engineering
COMPARISON OF MACHINE LEARNING TECHNIQUES FOR FAULT DIAGNOSIS IN BELT CONVEYOR IDLERS
Submission Author:
João Lucas Lobato Soares , PA , Brazil
Co-Authors:
João Lucas Lobato Soares, Thiago Barroso Costa, Lis Silva de Moura, Walter dos Santos Sousa, Alexandre Mesquita, André Luiz Amarante Mesquita, Danilo Braga
Presenter: João Lucas Lobato Soares
doi://10.26678/ABCM.COBEM2023.COB2023-2267
Abstract
Belt conveyors are essential components in the mining industry responsible for transporting ore. However, failures in their components can compromise the entire production process, leading to unexpected stops and delays in the schedule. Therefore, it is crucial to perform regular diagnostics and replace damaged parts to avoid these problems. Idler failure represents one of the primary failure modes in conveyor belt components, mainly associated with wear and bearing defects. Machine learning-based techniques have been successfully applied for condition monitoring and fault diagnosis in these components. Moreover, the objective of this study is to develop a fault diagnosis system for conveyor belt idlers utilizing artificial intelligence techniques, while also conducting a comparative analysis of the performance of proposed classification methods. The first method consists of applying the Gradient Boosting Decision Tree (GBDT), and the second consists of applying the Multilayer Perceptron (MLP) to the energy bands from the application of Wavelet Packet Decomposition (WPD) to the vibration signals of the belt conveyor. The performances of the methods are compared for each failure mode. The results obtained showed that for bearing defects GBDT presented accuracy above 100% from 30 estimators, while MLP obtained maximum accuracy of 98.3% with 100 iterations, which indicates better performance with decision trees application. Furthermore, for surface wear GBDT obtained maximum accuracy of 98.3% from 21 estimators, while MLP reached 96.7% with 100 iterations. Therefore, it is inferred that GBDT presented to be a more effective model compared to MLP by initial parameters. However, both models have high accuracy as classifiers for detection of idler failures.
Keywords
machine learning, Fault Diagnosis, GBDT, MLP, Belt conveyor idler
