DocumentCode :
1311095
Title :
Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure
Author :
Siegel, David ; Ly, Canh ; Lee, Jay
Author_Institution :
NSF Center for Intell. Maintenance Syst., Univ. of Cincinnati, Cincinnati, OH, USA
Volume :
61
Issue :
4
fYear :
2012
Firstpage :
846
Lastpage :
857
Abstract :
The enhanced ability to predict the remaining useful life of helicopter drive train components offers potential improvement in the safety, maintainability, and reliability of a helicopter fleet. Current existing helicopter health and usage monitoring systems provide diagnostic information that indicates when the condition of a drive train component is degraded; however, prediction techniques are not currently used. Although various algorithms exist for providing remaining life predictions, prognostic techniques have not fully matured. This particular study addresses remaining useful life predictions for the helicopter oil-cooler bearings. The paper proposes a general methodology of how to perform rolling element bearing prognostics, and presents the results using a robust regression curve fitting approach. The proposed methodology includes a series of processing steps prior to the prediction routine, including feature extraction, feature selection, and health assessment. This approach provides a framework for including prediction algorithms into existing health and usage monitoring systems. A case study with the data collected by Impact Technology, LLC. is analysed using the proposed methodology. Future work would consider using the same methodology, but comparing the accuracy of this prediction method with Bayesian filtering techniques, usage based methods, and other time series prediction methods.
Keywords :
aerospace safety; aerospace testing; condition monitoring; cooling; curve fitting; drives; failure analysis; fault diagnosis; feature extraction; helicopters; maintenance engineering; regression analysis; reliability; remaining life assessment; rolling bearings; diagnostic information; feature extraction; feature selection; health assessment; health monitoring system; helicopter drive train component; helicopter fleet; helicopter health; helicopter oil-cooler bearing; helicopter rolling element bearing failure; maintainability; prediction algorithm; prediction technique; prognostic technique; regression curve fitting approach; reliability; remaining useful life prediction; rolling element bearing prognostics; safety; usage monitoring system; Feature extraction; Helicopters; Prediction algorithms; Remaining life assessment; Rolling bearings; Vibrations; Bearing envelope analysis; bearing failure prediction; remaining useful life; robust regression;
fLanguage :
English
Journal_Title :
Reliability, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9529
Type :
jour
DOI :
10.1109/TR.2012.2220697
Filename :
6324405
Link To Document :
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