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• W2902971333 abstract "An original diagnostic method is proposed for identifying broken rotor bars based on a new technique for decomposing electric signals obtained by measurements, the Orthogonal Component Decomposition technique. This decomposition shows to be an efficient fault-specific feature extractor, and a broken bar simulation is used to determinate the relationships between the decomposition products and the fault phenomena. The efficiency of the information extraction is evaluated by Kruskall-Wallis variance analyses and Support Vector Machines on experimental signals, where the fault occurrence is detected, and the fault severity, given by the number of broken bars, is also diagnosed. The experimental signals are obtained by measurements from induction machines operating with different torque levels and driven either directly by the grid or frequency inverters. The proposed diagnostic method does not depend on frequency analysis and spectral resolution and comprises the Orthogonal Component Decomposition and Support Vector Machines. Furthermore, the results demonstrate the effectiveness of the information extraction since high diagnostic accuracy is achieved in low-torque situations and in a broad range of slip values. Additional analyses support that the proposed decomposition should be further investigated for many other applications on event detection." @default.
• W2902971333 created "2018-12-11" @default.
• W2902971333 creator A5044783910 @default.
• W2902971333 creator A5074469154 @default.
• W2902971333 creator A5078269845 @default.
• W2902971333 date "2019-02-01" @default.
• W2902971333 modified "2023-09-25" @default.
• W2902971333 title "Efficient feature extraction technique for diagnosing broken bars in three-phase induction machines" @default.
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• W2902971333 doi "https://doi.org/10.1016/j.measurement.2018.12.005" @default.
• W2902971333 hasPublicationYear "2019" @default.
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