Predictive Maintenance of Wind Turbines

Get in-time and advanced alerts on potential maintenance issues before they occur and align resources optimally to reduce downtime.

Overview

Our turnkey enterprise AI solution provides early warning of impending equipment failure weeks before the failure. This helps wind turbine operators minimize equipment downtime, reduce the risk of accidents, and lower maintenance costs while increasing the lifespan of the turbine. Operations and maintenance engineers and technicians at wind turbine operators can now focus on planning maintenance schedules rather than reacting to incidents. Contact us if you're interested in seeing how this solution could work for your business.

Key Metrics

Wind turbine companies are always looking for ways to improve the reliability and performance of their turbines, as well as reduce the cost of maintaining them over their lifecycle. One key area of focus is the operations and maintenance of the turbines.

By implementing predictive maintenance strategies that use data from the turbines’ sensors to identify potential issues before they occur, wind turbine companies can take proactive steps to prevent unplanned downtime and ensure that their turbines are operating at their best. In addition, implementing processes and procedures to ensure that turbines are properly maintained and repaired when needed can help extend their lifespan and improve their overall performance.

AI and machine learning (ML) can play a key role in optimizing wind turbine operations and maintenance. By training an ML model to analyze data from the turbines’ sensors and identify patterns or anomalies, wind turbine companies can proactively address potential issues before they become major problems. In addition, ML models can be used to optimize the scheduling and routing of maintenance personnel, helping to ensure that they are able to efficiently and effectively address issues as they arise. By leveraging the power of AI and ML, wind turbine companies can improve the reliability and performance of their turbines, as well as reduce the cost of maintaining them.

RapidCanvas AI Solutions impacting
Predictive Maintenance of Wind Turbines

Cost of downtime without AI
per machine, per year

$100,000

Cost of downtime with AI
per machine, per year

~$80,000

Highlights

Extract and prepare data from turbine sensors

Get in-time and advanced alerts on potential defects

Access dashboards on turbine performance and reliability

Build predictive models to forecast maintenance needs based on historical data

Use optimization techniques to align resources, schedule and workload

Get data driven insights into operations and maintenance

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At RapidCanvas, we empower human experts to focus on problem solving and create great products using data, in a matter of days. Combine domain expertise and automated machine learning to build the future.

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