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• W2892171646 abstract "Electric motors failure remains a very serious issue in the industrial world. This problem may not only result in the paralysis of the production but may also influence the operator safety. To resolve this problem, several methods have been developed for the monitoring and the diagnosis of faults from their appearances to avoid the industrial process interruption. With this objective in mind, this paper proposes a new diagnosis technique used in the identification of these faults based on stator current Auto-Regressive modeling. The proposed approach presents several advantages compared to the classical stator current spectral analysis using the conventional Periodogram technique. In fact, the proposed approach offers a very good frequency resolution for a very short acquisition time, which is impossible to achieve with the classical technique of the Periodogram. Simulation and experimental tests will be carried out later in this paper to verify the proposed method in bearing faults diagnosis." @default.
• W2892171646 created "2018-09-27" @default.
• W2892171646 creator A5000948997 @default.
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• W2892171646 date "2018-09-11" @default.
• W2892171646 modified "2023-09-24" @default.
• W2892171646 title "Monitoring and Fault Diagnosis of Induction Motors Mechanical Faults Using a Modified Auto-regressive Approach" @default.
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• W2892171646 doi "https://doi.org/10.1007/978-3-319-97816-1_30" @default.
• W2892171646 hasPublicationYear "2018" @default.
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