A method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks
R. Senthil Kumar, I. Gerald Christopher Raj, K.P. Suresh, P. Leninpugalhanthi, M. Suresh, Hitesh Panchal, R. Meenakumari, Kishor Kumar Sadasivuni
Abstract
This 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)