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المؤلفSenthil, Kumar R.
المؤلفGerald, Christopher Raj I.
المؤلفSuresh, K.P.
المؤلفLeninpugalhanthi, P.
المؤلفSuresh, M.
المؤلفPanchal, H.
المؤلفMeenakumari, R.
المؤلفSadasivuni, Kishor Kumar
تاريخ الإتاحة2022-03-23T06:35:50Z
تاريخ النشر2021
اسم المنشورInternational Journal of Ambient Energy
المصدرScopus
المعرّفhttp://dx.doi.org/10.1080/01430750.2021.1934117
معرّف المصادر الموحدhttp://hdl.handle.net/10576/28605
الملخصThis paper presents a high accuracy detection of Broken Rotor Bar (BRB) fault by Artificial Neural Network (ANN) through advanced signal processing tool as Hilbert Transform (HT) where three phase Induction Motor Drives (IMD) is operated under Direct Torque Control (DTC) topology with steady state. The major significance of all diagnostic methods is, need information about the characteristic?s frequencies and amplitude. The diagnosing of machine fault requires the spectrum into isolated various frequency components. The Discrete Fourier Transform (DFT) cannot produce good output at low slip. So, in this paper ANN and HT are proposed. DTC method is efficient technique in industrial drives with variable torque applications. The stator current envelope can be formed by HT. Then samples of amplitude and side band frequency are given as ANN inputs. In order to diagnose the quantity of BRB in IM, the findings are qualified and checked to the minimal Mean Square Error (MSE).
اللغةen
الناشرTaylor and Francis Ltd.
الموضوعArtificial Neural Networks
Broken Rotor Bars
Direct Torque Control
Fast Fourier Transform
العنوانA method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks
النوعArticle
dc.accessType Abstract Only


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