Oil condition monitoring for wind turbines Owners and operators gain a powerful view of how well their machinery is functioning with oil condition monitoring, allowing engineers to search for signs of wear from the turbine, such as loose metal particles. Monitoring turbine health in this way gives direct insight into whether to replace vital components. But it is also helpful to assess the state of the oil itself. Lubrication inside the turbine protects moving components and increases turbine life, so ensuring optimum oil health is crucial.
Checking the health of the oil gives a more holistic overview into how the turbine is functioning – key indicators include oil aging, water contamination, acid content and oil mixing. These show that a component is not working as it should, that the oil needs replacing, or that incorrect procedures have been followed during inspection work.
However, current practice throughout much of the wind industry is to take lubrication samples manually and send them to laboratories for analysis, typically at 12-month intervals. This gives an incomplete picture of ongoing developments in turbine health as it’s hard to draw accurate conclusions from data points spread months apart. Owners and operators can’t access the valuable insight they need to drive efficiencies.
For example, low levels of additives in oil, signalling sub-optimal lubrication, can potentially result in metal-to-metal contact and surface damage. With an offline oil sampling regime, this problem could be left undetected for up to 12 months, potentially leading to costly repairs such as bearing replacements – and even gearbox failure.
Employing online oil sensing has several benefits for the wind industry. Critically, it reduces the amount of time spend by technicians up-tower, therefore decreasing unnecessary risks for personnel.
Multiple data-streams: the future of wind O&M Over the last two years, ONYX InSight has conducted a series of extensive laboratory and field trials.
The study proved that oil and vibration data are a particularly effective combination when diagnosing emerging health issues on wind turbines, and therefore in providing information to enable O&M decisions that reduce O&M costs and boost operational profitability.
Extracting the full value of wind turbine data is critical to the future competitiveness of the industry. When oil data is combined with vibration monitoring, machine learning algorithms identify faults more effectively and with greater confidence. Paired with real-world engineering expertise, this provides O&M teams with better insights to inform planning.
Using multiple indicators enables data sources to play to each other’s strengths. Particle counters, for
instance, are notoriously susceptible to false alerts when used alone. 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.
Leveraging multiple data-streams allows engineers to pinpoint the root cause of issues and provide more useful recommendations. An oil particle count trend might tell the owner or operator something is happening in the gearbox, but it doesn’t tell them which component has a fault. Vibration analytics, on the other hand, pinpoints exactly which component is failing, allowing proactive maintenance actions to be taken before a costly gearbox failure.
Oiling the gears of wind energy growth The smart digitalisation strategies of the future will make use of all available data from wind assets, to gain a complete picture of asset health and inform a new, holistic approach to turbine O&M. And those who benefit most will be putting their strategies in place now.
LINK
www.onyxinsight.com
LUBE MAGAZINE NO.161 FEBRUARY 2021
31
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53
