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http://dx.doi.org/10.12815/kits.2017.16.2.128

Feature Extraction for Bearing Prognostics based on Frequency Energy  

Kim, Seokgoo (Dept. of Aerospace and Mechanical Eng., Korea Aerospace University)
Choi, Joo-Ho (School. of Aerospace and Mechanical Eng., Korea Aerospace University)
An, Dawn (Daegyeong Division/Aircraft System Technology Group., Korea Institute of Industrial Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.16, no.2, 2017 , pp. 128-139 More about this Journal
Abstract
Railway is one of the public transportation systems along with shipping and aviation. With the recent introduction of high speed train, its proportion is increasing rapidly, which results in the higher risk of catastrophic failures. The wheel bearing to support the train is one of the important components requiring higher reliability and safety in this aspect. Recently, many studies have been made under the name of prognostics and health management (PHM), for the purpose of fault diagnosis and failure prognosis of the bearing under operation. Among them, the most important step is to extract a feature that represents the fault status properly and is useful for accurate remaining life prediction. However, the conventional features have shown some limitations that make them less useful since they fluctuate over time even after the signal de-noising or do not show a distinct pattern of degradation which lack the monotonic trend over the cycles. In this study, a new method for feature extraction is proposed based on the observation of relative frequency energy shifting over the cycles, which is then converted into the feature using the information entropy. In order to demonstrate the method, traditional and new features are generated and compared using the bearing data named FEMTO which was provided by the FEMTO-ST institute for IEEE 2012 PHM Data Challenge competition.
Keywords
High speed rail; Bearing; Prognostics and health management; Feature; Entropy;
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