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.
The charts of the change in the calculated and actual temperatures of the stator winding based on the example of two grooves
- Actual temperature of stator winding
- Calculated temperature of stator winding
- 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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