z-logo
open-access-imgOpen Access
Developing Data Mining-Based Prognostic Models for CF-18 Aircraft
Author(s) -
Marvin Zaluski,
Sylvain Le ́tourneau,
Jeff Bird,
Chunsheng Yang
Publication year - 2010
Publication title -
volume 3: controls, diagnostics and instrumentation; cycle innovations; marine
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1115/gt2010-22944
Subject(s) - prognostics , aircraft maintenance , key (lock) , computer science , data modeling , data mining , process (computing) , variety (cybernetics) , focus (optics) , software , systems engineering , reliability engineering , engineering , software engineering , artificial intelligence , computer security , aeronautics , programming language , operating system , physics , optics
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.Peer reviewed: YesNRC publication: Ye

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom