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Developing Data Mining-Based Prognostic Models for CF-18 Aircraft

[+] Author Affiliations
Marvin Zaluski, Sylvain Létourneau, Jeff Bird, Chunsheng Yang

National Research Council of Canada, Ottawa, ON, Canada

Paper No. GT2010-22944, pp. 235-243; 9 pages
doi:10.1115/GT2010-22944
From:
  • ASME Turbo Expo 2010: Power for Land, Sea, and Air
  • Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine
  • Glasgow, UK, June 14–18, 2010
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4398-7 | eISBN: 978-0-7918-3872-3
  • Copyright © 2010 by National Research Council of Canada

abstract

The CF-18 aircraft is a complex system for which a variety of data are systematically being recorded: operational flight data from sensors and Built-In Test Equipment (BITE) and maintenance activities recorded by personnel. These data resources are stored and used within the operating organization but new analytical and statistical techniques and tools are being developed that could be applied to these data to benefit the organization. This paper investigates the utility of readily available CF-18 data to develop data mining-based models for prognostics and health management (PHM) systems. We introduce a generic data mining methodology developed to build prognostic models from operational and maintenance data and elaborate on challenges specific to the use of CF-18 data from the Canadian Forces. We focus on a number of key data mining tasks including: data gathering, information fusion, data pre-processing, model building, and evaluation. The solutions developed to address these tasks are described. A software tool developed to automate the model development process is also presented. Finally, the paper discusses preliminary results on the creation of models to predict F404 No. 4 Bearing and MFC (Main Fuel Control) failures on the CF-18.

Copyright © 2010 by National Research Council of Canada

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