Reducing Wind Turbine Downtime with a Predictive Strategy

Conventional maintenance strategies can undermine how well wind turbines operate. The over-reliance on scheduled maintenance or replacing parts systematically as catchalls for addressing wind turbine maintenance issues has become inefficient. Instead of prioritizing the actual maintenance needs of wind turbines, those strategies can actually contribute to wind turbine downtime and wasted resources.

To limit wind turbine downtime, maintenance strategies have to rely on condition-based monitoring and predictive maintenance. The increased monitoring and collection of machine data using advanced visualization systems like Odysight.ai’s image-based AI-platform provides foresight that helps avoid wind turbine downtime.

How Predictive Maintenance is Used for Wind Turbines

Employing a predictive approach to wind turbine maintenance improves operational reliability. With predictive maintenance, wind turbine failures can be avoided by alerting maintenance engineers of impending breakdowns, allowing the scheduling of timely and tailored maintenance tasks before the breakdown occurs.

This maintenance approach uses real-time machine data collected during the continuous monitoring of equipment function and environmental conditions. The data is captured by specifically placed sensors, based on Odysight.ai®’s Camera-as-a-Sensor technology. For example, for land-based gearbox turbines, the sensors can be placed near the gearbox or yaw drive to monitor the wearing of the components while they are in operation.

Machine learning algorithms will then parse and analyze the data, providing insights and highlighting any abnormalities or issues that can become significant problems. The insights derived from the AI models can also detail which appropriate maintenance or repair actions should be applied and when, to prevent the projected failure.

Using predictive maintenance as a maintenance tool for wind turbines allows maintenance engineers to:

• Accurately predict the lifetime of the components in their respective environments
• Respond timely to operational issues
• Optimize the use of maintenance resources
• Potentially save budgetary resources allocated to wind turbine maintenance

Getting the Wind Turbine Data is Key for Predictive Maintenance

Gauging and properly responding to the performance of wind turbine components is a critical capability of predictive maintenance. For wind turbine maintenance in particular, applying sensors for monitoring is imperative as turbines are typically situated an average of 280 feet in the air and higher, let alone out at sea in case of offshore wind turbines, in order to take advantage of the higher wind speeds to capture more wind energy and generate more electricity.

From a maintenance standpoint, having these sensors in place makes wind turbine maintenance more efficient and safer, as engineers do not have to physically examine the components in person, which involves logistics for accessing the area and possibly disassembling of the turbine, often in extreme environments.

Being Proactive with Wind Turbine Predictive Maintenance

Continuous, condition-based monitoring and predictive maintenance can eliminate much of the guesswork from wind turbine maintenance. Maintenance engineers can understand exactly why and how a machine will fail, learning the indications of potential failures.

Being able to plan ahead with predictive maintenance creates critical advantages for wind turbine maintenance that reduces downtime. These capabilities also reduce maintenance costs and enhance equipment performance and productivity:

• Prevention of Secondary Damage to Other Components. With the layout of wind turbines, the risk of secondary damage to nearby components is high. For example, bearing looseness can result in secondary damage to the wind turbine drivetrain. Secondary damage in wind turbines can also include residual chemicals after an electrical fire started by an arc flash event.
• Foresight to Secure Critical Resources in Advance at a Reduced Cost. Part of the emergency costs that arise from unexpected downtimes are those needed to quickly secure replacement components. In these cases, wind turbine maintenance and the ability to get the machine operating again are at the mercy of low inventory, expedited shipping costs, etc.Additional money also has to be spent to ensure the personnel with the proper expertise are able to replace the parts as quickly as possible. Being able to determine when and how a machine will break affords maintenance personnel the opportunity to allocate the necessary resources in advance, typically at lower costs, so that they are on hand when needed.

• An Overall More Efficient Maintenance Program. The foresight obtained from a predictive maintenance strategy also makes the maintenance process more efficient. Multiple wind turbine maintenance and repair activities can be executed simultaneously.
• More Autonomy Over Scheduling Necessary Downtime. When maintenance needs have been established well in advance of when it is required, maintenance engineers have some leeway in scheduling maintenance tasks. Maintenance can be schedule at times when wind turbine downtime will have the least impact, such as during periods in which wind speeds are low or below average.

Use Odysight.ai to Help Reduce Wind Turbine Downtime and Save Additional Costs

Wind turbines can remain in operation, producing energy, until maintenance is truly needed. Limiting how long wind turbines remain offline due to maintenance issues requires implementing condition-based monitoring and predictive maintenance, and doing so with the right technology.

Odysight.ai’s visualization and AI platform can be used to continuously monitor the health of wind turbine components in areas that are difficult to access. To learn more about Industry 4.0 applications for Odysight.ai’s microvisualization solutions, get in touch with one of our experts for a demonstration.