Using the RapidCanvas predictive maintenance solution you can:
Use machine learning to prevent unplanned downtime and ensure that your wind turbines are operating at their best. Properly maintaining and repairing turbines can help extend their lifespan and improve their overall performance.
Extract and prepare data from wind turbine sensors
Build predictive models to forecast maintenance needs based on historical data
Get in-time and advanced alerts on potential defects
Use optimization techniques to align resources, schedule and workload
Access dashboards on turbine performance and reliability
Get data-driven insights into operations and maintenance
“Modeling in Rapid Canvas was very easy. I did not need to worry about creating ad hoc infrastructure for my machine learning project: Rapid Canvas provided a standardized platform. Out-of-the-box curated solutions within RapidCanvas made my development process error-free, reusable and repeat- able. After the entire machine learning lifecycle flow was implemented, the testing harness made it easy to analyze each code block. The interface also made it easy to identify the function of each code part, maintain and adapt it. Besides that, RapidCanvas helps generate insights and reports that I can share with business users and my clients.”
Leverage AI to implement predictive maintenance for wind turbine operations, in a matter of days. At RapidCanvas, we empower human experts to focus on problem solving. Combine domain expertise and automated machine learning to build the future.