Novelty detection for predictive maintenance scheduling for industrial gas turbines

Gallimore, Michael and Yang, Zhijing and Bingham, Chris and Stewart, Paul and James, N. and Watson, S. and Latimer, A. (2011) Novelty detection for predictive maintenance scheduling for industrial gas turbines. In: International Conference on Mechanical Engineering and Technology (ICMET-London 2011), November 2011, London.

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Official URL: http://dx.doi.org/10.1115/1.859896.paper73

Abstract

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.

Item Type:Conference or Workshop Item (Presentation)
Additional Information: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.
Keywords:Gas Turbine, Remote monitoring and sensing, predictive maintenance, bmjdoi
Subjects:H Engineering > H321 Turbine Technology
Divisions:College of Science > School of Engineering
ID Code:6058
Deposited By:INVALID USER
Deposited On:18 Aug 2012 19:52
Last Modified:28 Aug 2014 09:24

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