Assistance of analytical ferrography in the interpretation of wear test results carried out with biolubricants

Abstract

Analytical ferrography plays a crucial role in predictive maintenance, which is a proactive approach to asset and equipment management. This technique provides valuable information about the internal condition of machines and components, allowing early identification of potential problems and the scheduling of maintenance interventions before catastrophic failures occur. Ferrography allows you to identify worn particles, non-metallic inclusions and other indicators of wear on components. By analyzing samples of lubricating oil or hydraulic fluid, analytical ferrography can offer insights into the health of internal components such as gears, bearings and other critical elements. Changes in particle concentration and composition may indicate abnormal wear. This work involves the use of analytical ferrography to interpret wear results carried out with two vegetable lubricants with additives, compared to a mineral lubricant, also with additives. To assist in the interpretation of the results, the open source software Image J and a small program developed in Python were used. It was found that it is possible to use these two tools to aid the interpretation of ferrograms.