Técnicas de aprendizado de máquina não supervisionado para a prevenção de falhas em máquinas de chave

Abstract

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.