Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-03-27T17:53:34Z No. of bitstreams: 1 nielsonsoares.pdf: 4342256 bytes, checksum: bcb08c0e8cbff9d4ed4b60f92f7715b2 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-03-27T18:03:27Z (GMT) No. of bitstreams: 1 nielsonsoares.pdf: 4342256 bytes, checksum: bcb08c0e8cbff9d4ed4b60f92f7715b2 (MD5) / Made available in DSpace on 2018-03-27T18:03:27Z (GMT). No. of bitstreams: 1 nielsonsoares.pdf: 4342256 bytes, checksum: bcb08c0e8cbff9d4ed4b60f92f7715b2 (MD5) Previous issue date: 2018-02-27 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / As máquinas de chave são equipamentos eletromecânicos de grande importância em uma malha ferroviária. A ocorrência de falhas nesses equipamentos pode ocasionar interrupções das ferrovias e acarretar potenciais prejuízos econômicos. Assim, um diagnóstico precoce dessas falhas pode representar uma redução de custos e um aumento de produtividade. Essa dissertação tem como objetivo propor um modelo preditivo, baseado em técnicas de inteligência computacional, para a solução desse problema. A metodologia aplicada compreende o uso de técnicas de extração e seleção de características baseada em testes de hipóteses e modelos de aprendizado de máquina não supervisionado. O modelo proposto foi testado em uma base de dados disponibilizada por uma empresa ferroviária brasileira e se mostrou eficiente ao constatar como críticas as operações realizadas próximas à operação classificada como falha. / Railroad switch machines are important electromechanical equipment in a railway network, and the occurrence of failures in such equipment can cause railroad interruptions and lead to potential economic losses. Thus, an early diagnosis of these failures can represent a reduction of costs and an increase in productivity. This dissertation aims to propose a predictive model, based on computational intelligence techniques, to solve this problem. The applied methodology includes the use of features extraction and selection techniques based on hypothesis tests and unsupervised machine learning models. The proposed model was tested in a database made available by a Brazilian railway company and proved to be efficient when considering as critical the operations performed close to the operation classified as failure.

The task of the thesis is to make a study of reconstruction of two railway stations: Bohdíkov and Ruda nad Moravou. The aim of the project is to prepare a plan of reconstruction, which will suit actual state of traffic. The plan must abide the rules of law, especially in the case of the entry of people with physical handicaps.The thesis contains the plan of reconstruction of platforms, a regulation of geometric parameters of tracks, reconstruction of superstructure and also rebuilding of actual drainage.