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ASME Press Select Proceedings

International Conference on Mechanical Engineering and Technology (ICMET-London 2011)

Editor
Garry Lee
Garry Lee
Information Engineering Research Institute
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ISBN:
9780791859896
No. of Pages:
906
Publisher:
ASME Press
Publication date:
2011

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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