Monitoring oil health in wind assets


ONYX InSight, a provider of data analytics and engineering expertise to the global wind industry, has launched a multi data-stream technology for online oil monitoring that increases clarity, reduces risk and saves considerable operations and maintenance (O&M) costs for the wind industry.

Over the last two years, ONYX InSight has conducted a series of laboratory and field trials, alongside a leading expert in industrial lubricants, to develop the most innovative and advanced data analytics solution for the market. These trials have proven that digitalisation of oil analysis increases the quality and value of data extracted from a wind turbine and that, by using sensors to digitally monitor machine and oil health, wind farm owners and operators can significantly reduce O&M costs by decreasing risk.

Furthermore, the trials also demonstrated the benefits of combining multiple data streams for a more accurate picture of turbine health.

Oil monitoring is essential for the long-term productivity of wind assets. However, relying only on legacy offline monitoring leaves turbines at risk of damage due to inconsistent data reports that prevent owners and operators from making pivotal cost-saving decisions for their fleets, ONYX InSight states.

Offline oil samples, typically taken every 6-12 months, lack the consistency to trend and draw trustworthy conclusions. This means that key indicators of oil degradation can lie undiscovered for extended periods. Left unchecked, low levels of additives in oil (that can signal the lubrication is not operating optimally) can potentially result in metal-to-metal contact and surface damage, leading to costly repairs such as bearing replacements and even gearbox failure.

According to ONYX InSight, another risk factor of offline oil sampling, which occurs most frequently during maintenance procedures, is the slow response to detecting contaminants such as water in the oil, which is highly detrimental to machine health. In this instance, offline laboratory analysis of the oil would give too late a warning, potentially up to 12 months after the onset of damage.

Further insights from the initial study demonstrated the benefits of combining different data sources for improved machine health diagnostics. When oil data is combined with vibration monitoring, machine learning algorithms identify faults more effectively and with greater confidence which, alongside real-world engineering expertise, provides O&M teams with better insights to inform planning.

Particle counters alone are notoriously susceptible to false alerts. Combining vibration condition monitoring systems with particle data mitigates this and significantly reduces false alerts, with advanced analytics providing a more accurate indication of where faults are present in the gearbox.

“By integrating oil and vibration data we can provide our customers with a complete and unparalleled understanding of machine and oil health,” said Bruce Hall, Chief Executive Officer, ONYX InSight.

“As wind farms come under increasing pressure to increase their profitability, it is vital that partners like us enable O&M teams to drive maximum operational efficiencies using the latest technology alongside our engineering expertise. Being an earlier adopter of digitalising oil data, and combining this with vibration data, allows us to revolutionise the way owners and operators run operations by increasing data, accuracy and value.”