Novelty Detection for Predictive Maintenance Scheduling for Industrial Gas Turbines


The paper presents results of an investigation to predict impending failure mechanisms of a gearbox drive train in the sub 15MW class of the Siemens gas turbine product range. Particular emphasis is given to the prediction of gearbox failures and inter-connected components. Experimental results from real-time data show that the application of SVM techniques provides an efficient basis for minimising the impact of unscheduled maintenance requirements, on product lifetime and cost for these units.

  • Abstract
  • Keywords
  • Introduction
  • Benefits of Predictive Maintenance
  • Support Vector Machines and Clustering
  • Problem Definition
  • Results
  • Discussion
  • Conclusion
  • Acknowledgements
  • References

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