”The system ensures a better service for Vestas’ customers since their problems will be solved faster via a remote connection to the turbine as well as phone-based contact. More problems will be solved faster and without the need to wait for a skilled service technician”, says Claus Skaaning, CEO of Dezide.
“When a service technician arrives to the turbine, his local version of the guided troubleshooting tool will ensure that the problem is solved as quickly and efficiently as possible, reducing spare part consumption as much as possible.”
Vestas wants to lead wind turbine service
The new tools support Vestas’ strategy to become a leader within service of wind turbines. The system ensures knowledge sharing among Vestas’ technicians around the world such that ideas for solving different problems can be shared quickly and easily. Vestas will also get a much more detailed logging of the individual problem scenarios.
”We view Dezide’s intelligent software as one of the elements in our strategy to provide the best service to ensure that our customers get the best possible performance and value from their Vestas turbines. We expect that the system will enable us to increase the customer satisfaction as well as reducing costs in the service area”, says Johnny Stephansen, SVP Plant Services, Vestas.
With 46.143 wind turbines or 49.332 MW installed world-wide per 31st December 2011 and more than 30 years experience in development, production, installation and maintenance of the world’s best wind turbines, Vestas is the market leader within wind.
Dezide is a software company that sells intelligent guided systems for diagnostics and guided troubleshooting. Dezide was chosen as one among the 100 best technology companies in Europe a few years ago. In 2011, the company was nominated to IT company of the year in North Jutland. Dezide has customers in Europe, Asia and the US.
Facts about the Dezide solution
Dezide’s technology is called Bayesian networks and is a modern type of artificial intelligence where the system continually calculates probabilities of possible root causes and solutions, based on the information received from the user and in an automated manner from other sources such as sensor data and alarm codes. This information is then used to suggest an optimized sequence of tests and repair steps that reduce time and spare parts as much as possible. The system is capable of learning over time and can thus improve itself in an automated manner as it learns the typical root causes for various problems and the best solutions.