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- W2891321154 abstract "Abstract This paper presents faulty bearing detection, classification and its location in a three-phase induction motor using Stockwell transform and Support vector machine. Stockwell transform is applied to stator current signals to extract a number of features in both time and frequency domain. A set of non-correlated and high ranking features are selected based on Fisher score ranking. These features are in turn used to classify the faults such as ball, cage and outer-race faults using Support vector machine. Subsequent to fault identification, features of Stockwell transform are used to locate the defective bearing, i.e, either at fan-side or load-side of the motor. This algorithm is successfully implemented on the experimental data of defective bearings collected from the industry." @default.
- W2891321154 created "2018-09-27" @default.
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- W2891321154 date "2019-01-01" @default.
- W2891321154 modified "2023-10-16" @default.
- W2891321154 title "Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine" @default.
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- W2891321154 doi "https://doi.org/10.1016/j.measurement.2018.09.013" @default.
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