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- W2334135085 abstract "This paper proposes an approach to detecting bearing faults in electromechanical actuators (EMAs) using features extracted from experimental data. The method of feature extraction proposed uses established parameter estimation techniques based on system identification followed by an orthogonal transformation of estimated parameters to derive the required features. A Bayesian classifier is then used to create health classes from the extracted features. The performance of the approach is tested using both data obtained from simulations of bearing faults in a permanent magnet DC motor system as well as data recorded from a Moog MaxForce EMA. The approach shows a misclassification performance of 10% when tested with 50 different data sets generated via simulations. Marginally inferior performance is observed when using 40 different data sets collected from the Moog MaxForce EMA. The conclusion is that bearing fault detection in EMAs is possible via the proposed approach, although further refinements are required." @default.
- W2334135085 created "2016-06-24" @default.
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- W2334135085 date "2013-08-15" @default.
- W2334135085 modified "2023-09-26" @default.
- W2334135085 title "Bearing Fault Detection in Electromechanical Actuators from Empirically Extracted Features" @default.
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- W2334135085 doi "https://doi.org/10.2514/6.2013-4923" @default.
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