Estimação da vida útil remanescente de trilhos ferroviários por meio de técnicas de aprendizado de máquina

• Ano de defesa: 2019
• Autor(a) principal: Rocha, Gledson Fabio Cotrim
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Engenharia Civil; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Civil
• Palavras-chave em Português: Trilho ferroviário; Rede Neural Artificia; k-Vizinhos mais próximos; Superestrutura ferroviária; Rail track; Artificial neural network; k-Nearest neighbors; Railway superstructure; subject.br-rjbn; Engenharia Civil
• Link de acesso: http://repositorio.ufes.br/handle/10/13339

As the rail is the costliest element on the permanent track, finding tools to estimate the remaining life is important to make the most of them. Thus, it has hypothesized that machine learning algorithms trained from historical databases can assist the planner in performing this estimation. Two machine learning algorithms were tested: Artificial Neural Network (RNA) and Nearest K-Neighbors (k-NN). The historical databases used were from Vitória to Minas Railway (EFVM). The dataset has 1,275,034 records for the period of 6 years and 8 months. The RNA had 5 neurons in the input layer: 1) the degree of the curve; 2) internal or external rail curve; 3) the width of the rail; 4) the height of rail; and 5) average weight carried. The intermediate layer, regardless of the category for estimating the remaining rail life, had variations of 1, 2 and 3 layers and variations in the number of neurons of 30, 50, 100, 200 and 400. The output layer depends on the period category the remaining useful life of the rails: 1) month, with 80 neurons, 2) quarter, with 27 neurons, 3) semester, with 14 neurons and 4) year, with 7 neurons. For the k-NN algorithm, configurations ranging from k = 5, 7 and 9 were tested. For both algorithms, k-fold cross validation was applied, with f = 10, and performance was evaluated using the accuracy value and F1-score. The programming language was Python and the Scikit-Learn library. RNA configurations and k-NN configurations were compared and k-NN showed superior results to RNA. However, both algorithms reached the objective proposed in this dissertation, which was the estimation of the remaining rail life in order to help the railroad maintenance planner to replace the rails, where they obtained results in accuracy and F1-score higher than 80% for both algorithms for the semester and year period categories, these period categories being most used by rail operators. The k-NN algorithm always obtained better results when compared to RNA