A Latin American renewable energy company optimized its maintenance schedules and reduced its risks and costs, extending the useful life of each turbine. Individual components of turbines are susceptible to failure, leading to downtime which in turn results in associated losses and risks.
The unpredictability of failures and reactive but urgent nature of maintenance needs make this a critical business problem. Predicting failure events and minimizing costs of repair using data science was identified as an overarching goal.
RapidCanvas and wind turbine data scientists worked with the operations team of the company to gather and use data.
The output of this model is used to optimize maintenance schedules and checks on a regular basis, reducing the risk of and costs of maintenance. The organization experiences a drastically improved uptime and longer useful life of each wind turbine.
“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.”
A Latin American renewable energy company optimized its maintenance schedules and reduced its risks and costs, extending the useful life of each turbine
A Brazilian company that builds solar farms identified the most optimum land parcels for new farm development to be evaluated quickly and efficiently.
A European telecommunications company leveraged ML to predict RLF caused by weather conditions and maintain consistent, reliable 5G service.