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.
Identify maintenance parameters and sensor data from the SCADA system
Match it with the time of failures, duration of outages, and the affected sub-system.
Map and generate algorithms to predict the times to and between failures and their statistical distribution.
Operationalize in the organization's functional environment.
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.
Mean Downtime Cost
USD 100,000
per machine per year.
Reduced downtime costs by
USD 80,000 per machine per year.
“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
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