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F5 PMM system

Use AI scenario modeling

To improve the performance of energy equipment, cut the capital costs, and reduce downtime, we have developed a predictive analysis model for the turbine generator.

The solution is based on F5 PMM, a system for monitoring and predicting the technical condition of industrial equipment.
Sequencing of actions: 
  • The turbine generator’s historical data was collected and analyzed 
  • The relevant parameters characterizing the state of the turbine generator were identified
  • The values of stator current variation till failure were analyzed 
  • A linear regression of the stator winding temperature from the total power of the turbine generator was constructed based on the data for one month of its normal operation  
  • and actual and calculated temperatures were compared across the entire sample of values provided 


  • A systematic excess of the actual temperature of the stator winding over the calculated temperature in specific grooves and temperature peaks were detected.

  • Actual temperature of stator winding 
  • Calculated temperature of stator winding 

The charts of the change in the calculated and actual temperatures of the stator winding based on the example of two grooves

- The predictive analysis model thus developed can detect abnormal temperatures in the stator winding of a turbine generator 1.5 years before failure.

- The model can predict the onset of failure in other grooves well in advance 

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