With the aim of ensuring that wind energy continues to be profitable, big efforts are directed towards reducing the cost of maintenance in wind turbines. Although the reduction of maintenance tasks may initially decrease the cost of maintenance itself, in the long run, the total cost may be grown due to the increase in failures. It is estimated that as much as 70% of loss of function in machinery could be attributed to mechanical or corrosive wear. Oil and grease analysis has been proven as a viable condition monitoring technique for the early detection and tracking of damage in bearing and gear elements in wind turbine gearboxes. Indeed, more than three out of four gearbox problems could be linked to the bearings, which usually lead to gearing damage.

Lubrication analysis has three main objectives in predictive maintenance:

1. To monitor the condition of the lubricant, revealing whether the system fluid is healthy and fit for further service or requires a change
2. To ensure the oil quality standards
3. To protect the components involved.

This approach will increase the reliability and accuracy of the analyses. However, a good strategy needs to be implemented in order to provide a proper return on investment. The Predictive Diagnostics team uses indexes based on operational data to detect and report deviations that indicate developing faults. Traditionally, absolute values of the different lubrication parameters analyzed were checked to ensure they are within the limits, but the added value could be greatly increased by analyzing parameter trends. Therefore, predictive team keeps working on implementing improvements within EDPr predictive maintenance program in order to make an effort to extract the maximum advantage of lubrication analyses.

David is a Chemical Engineer from the University of Oviedo since 2006, year in which he started to work in the field of lubrication. He finished his PhD in 2011 and he continued working with the research group "Lubrication and Surface Technology – LuSuTec" on projects related to the use of ionic liquids in lubrication until 2022. After that, he joined EDP Renewables as "Predictive Diagnosis Engineer", adding his wide experience in Tribology and lubrication to the predictive team in order to detect, report and document possible failures in EDPr wind turbines fleet by monitoring lubrication conditions and using data analysis.