Ciência de dados aplicada para manutenção preditiva

Abstract

With the introduction of Industry 4.0 and the increased demands for operational efficiency, the application of Exploratory Data Analysis (AED) and predictions using Machine Learning (ML) for predictive maintenance has become a proactive and strategic decision. Such techniques aim to avoid failures and maximize the performance of industrial equipment. In this context, this work seeks to use AED and the creation of models using ML to study a synthetic dataset generated to reproduce data from all equipment in a company. The main objective is to study the creation of prediction models capable of identifying equipment failures, giving a new perspective to professionals in the field and serving as a reference point for future studies with data acquired in real situations.