Análise do indicador de confiabilidade de máquinas pesadas utilizadas no processo de terraplenagem aplicado em parque eólico do Rio Grande no Norte

Abstract

Wind energy is a promising solution to meet the growing demand for sustainable energy, standing out for its ability to generate clean, renewable electricity with low greenhouse gas emissions. The construction of wind farms involves complex processes, such as earthmoving, which requires white goods and yellow goods machines to ensure efficiency and quality. Proper maintenance of this equipment is essential to avoid delays, reduce costs and ensure schedule accuracy. This work's main objective is to analyze the reliability indicators of heavy machines used in the earthmoving process in the construction of a wind farm in Rio Grande do Norte. The research covers four families of machines: motor graders, hydraulic excavators, bulldozers and dump trucks, based on operational data collected over three months. Indicators such as MTBF, MTTR, failure rate, availability and reliability were evaluated, in addition to the financial and operational impacts of failures. The results highlight that the average costs per unproductive equipment ranged from R$47,436.48 for motor graders to R$211,067.92 for dump trucks, totaling R$473,328.52 in operational losses. Furthermore, the unproductiveness of operators due to machine maintenance amounted to R$139,967.99. The analysis also highlights the importance of using backup machines, which would eliminate these unproductive costs. Dump trucks required 12 spare machines, while motor graders needed just one. This demonstrates, according to studies, that dump trucks have lower reliability than motor graders, even though they have a large difference in the amount of equipment in operation. Based on the data analyzed, hydraulic excavators stood out as the most reliable family of equipment in the quarter, with an average reliability of 92.59%, while tracked tractors had the lowest average reliability, with 83.80%, demonstrating a greater need for maintenance. This data reinforces the relevance of strategic and preventive management, which mitigates financial and operational impacts caused by failures. Research suggests that, with reliability indicators, it is possible to predict failures and avoid schedule interruptions. It is concluded that efficient asset management, combined with the implementation of backups, strategies related to outsourcing maintenance and scheduled replacement of equipment, are essential to ensure productivity and meeting deadlines in large-scale projects.