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- W1836579272 abstract "In this research, the frequency features of vibration signal are chosen to analyze a bearings' vibration characteristics under unbalanced load in common operation conditions. The development process of an unbalanced fault was identified by a set of time-based vibration frequency spectrum. Based on the time-frequency features, the bearing's remaining useful life (RUL) can be predicted. A 2-layer neural network is designed to recognize and track the fault's feature patterns contained in the vibration signal. This research provides tools to analyze the features of a bearing vibration signal and provides effective pattern recognition techniques for bearing health diagnosis and RUL prediction." @default.
- W1836579272 created "2016-06-24" @default.
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- W1836579272 date "2002-11-13" @default.
- W1836579272 modified "2023-09-26" @default.
- W1836579272 title "Analysis of vibration signal's time-frequency patterns for prediction of bearing's remaining useful life" @default.
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- W1836579272 doi "https://doi.org/10.1109/ssst.2001.918485" @default.
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