Predictive Maintenance for Wind Turbines

DINGO monitors wind turbine health data for the top renewable energy companies across North America to prevent unplanned failures and maximize productivity.  By leveraging a rules-based predictive analytics engine and a proprietary turbine health database, we have the power to improve the health and performance of your entire fleet or a specific subset of your fleet.

We currently monitor over 25 GW of wind turbine capacity in Trakka across 24 U.S. states and 10 Canadian provinces.   Moreover, we can provide extensive benchmarking data and insights on how to improve your fleet's energy output based our global asset health database of over 10,000 wind turbines, covering all of the major equipment manufacturers, including GE, Vestas, Mistubishi, Alstom, Gamesa, and Siemens.

"DINGO's Trakka software has exceeded expectations, and the visibility and trending capabilities are excellent. This system also helps us prevent unplanned failures by quickly alerting us to high particle count and ferrous wear in gearboxes, allowing us to perform filtration before any real damage and costly downtime occurs."

Reliability Engineer in the Wind Performance Group at MidAmerican


Wind Turbine Gearbox Case Study
A demonstration of the benefits of Trakka for wind turbine maintenance. The software streamlines the management of condition monitoring information, including oil, grease, vibration, temperature, a...
Wind Energy Introduction
An overview of Dingo's solution for the wind industry.
Field Inspection App Demo
A demonstration of Dingo's Award Winning Field Inspection App, showcasing the ease-of-use and key benefits of this mobile, end-to-end solution for field inspection data capture and analysis.
DINGO Savings Calculator
Estimate the annual savings that your operation could achieve with Dingo’s Asset Health Solutions with this easy-to-use calculator.  The cost reduction is based on real world savings tha...
Introducing TRAKKA Predictive Analytics
Dingo’s NEW Trakka Predictive Analytics solution utilizes artificial intelligence to identify anomalies and predict and address impending equipment failures with confidence.