A FERROGRAFIA APLICADA NO GERENCIAMENTO DOS MANCAIS DE ROLAMENTOS DE MÁQUINAS ROTATIVAS

Abstract

Bearings are widely used as supports for rotors and shafts in many machine systems such as combustion engines, electric motors, turbines and pumps of various sizes. The bearing is simply designed, highly efficient, has long service life, low cost and does not fail easily. Based on preventive maintenance strategies, many monitoring techniques are developed to monitor rolling bearings, such as lubricant analysis, vibration analysis, noise and acoustic emission analysis. However, incorrect operations and degradation of oil in bearings can cause unexpected and sudden failures that are detrimental to machines and operator safety. Traditional maintenance tends to be reactive, responding to equipment or device failures after the fact. This traditional, reactive approach of describing failures after they have occurred (corrective/productive maintenance) is the worst-case scenario for maintenance: reacting to equipment or device failures after the fact. Preventive maintenance empowers operators to perform ongoing maintenance. A disruptive model harnesses the power of connected devices and the Internet of Things in a way that changes the dynamics of conventional Total Productive Maintenance, which defines maintenance as simply minimizing machine downtime. Ferrography is one of the resources for oil analysis, being a warning sign and showing the possible tendency to stop the machine.