Success Story Predictive Maintenance

Gas Turbine

Gas turbines are considered the cleanest and most efficient fossil fuel power plants. In addition, they offer an incredible variety of possible applications: from cogeneration to vehicle propulsion. Gas turbines can be a component of complex machines whose high efficiency depends on the optimal condition of the entire plant. Modern data evaluation methods can detect even the slightest changes in machine behavior.

pre-warning time before critical damage occurs


  • To reduce costly down-time / To increase uptime.
  • To lower maintenance costs.
  • To gain higher process/operation insight.


  • During peak and medium load operation, power plants must respond quickly to fluctuations in demand in the grid. Different power levels are passed through within a short time.
  • Due to the large power bandwidth, the characteristics of a gas turbine can vary greatly. The reactions of the plant, e.g., with regard to the vibration patterns that arise, are complex.
  • The aging state and maintenance history mean that each turbine develops its own unique signature, which also changes over time


  • Prexello utilizes the combination of all available operational information, such as fuel flow and environmental data, as well as the manual intervention of operators, to create better forecasts of the vibration behavior.
  • Using Prexello even the smallest deviations in behavior become visible, long before critical vibration values are reached.
  • The early detection of damage provided by Prexello allows a protective intervention in the operation, as well as a timely maintenance planning.


  • In this specific example, Prexello detected small but critical change in the dynamic vibration behavior of the turbine that resulted from a crack in one of its blades.
  • Prexello issued a warning more than 3 days before a critical damage occurred which allowed intervening at an early stage, resulted in smaller repair costs and shorter down-times.
  • Without Prexello, this damage would have led to a break-off and thus resulted in a long maintenance shutdown. This would have caused higher unscheduled maintenance and down-time costs.

Your Contact

Carlos Ayala Jiménez

Carlos Ayala Jiménez

Phone:  +49 1516 2 60 74 21