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• W2074349894 startingPage "2193" @default.
• W2074349894 abstract "Early detection of induction motor faults has been a main subject of investigation for many years. Several approaches have been proposed for identifying one or more faults treated in an isolated way. Multiple combined faults on induction motors represent a big challenge since the reliable diagnosis of a faulty condition under the presence of two or more simultaneous faults is really difficult. This work introduces a novel methodology that merges singular value decomposition, statistical analysis, and artificial neural networks for multiple combined fault identification. Obtained results demonstrate its high effectiveness on detecting faulty bearings, unbalance, broken rotor bars, and all their possible combinations. The developed field programmable gate array-based implementation offers a portable low-cost solution for online classification of the rotating machine condition in real time. Thanks to its generalized nature, the introduced approach can be extended for detecting multiple combined faults under different working conditions by a proper calibration." @default.
• W2074349894 created "2016-06-24" @default.
• W2074349894 creator A5007713889 @default.
• W2074349894 creator A5050726089 @default.
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• W2074349894 date "2014-10-01" @default.
• W2074349894 modified "2023-10-17" @default.
• W2074349894 title "Real-time SVD-based detection of multiple combined faults in induction motors" @default.
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• W2074349894 doi "https://doi.org/10.1016/j.compeleceng.2013.12.020" @default.
• W2074349894 hasPublicationYear "2014" @default.
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