A combined HT and ANN based early broken bar fault diagnosis approach for IFOC fed induction motor drive

Abstract

In recent years, fault diagnosis in the Induction Motor Drive (IMD) has been a popular and important field in the motor-drive research area. The development of control circuits for induction motors has prompted the attention of both researchers and industrialists. This paper proposes a broken bar fault diagnosis using Hilbert Transform (HT) and Artificial Neural Networks (ANN), with the drive regulated through the Indirect Field Orientation Control (IFOC). The HT obtains the spectrum of stator current, which is utilized to identify the Broken Rotor Bar (BRB) failure. The magnitude and side-band frequency of the drive are extracted using the Fast Fourier Transform (FFT), and these parameters are fed into the ANN inputs. The fault severity is computed by the ratio of mean side-band frequency amplitude to the main frequency amplitude for finding the impact of failure in the drive. ANN is used to diagnose failure with high accuracy. The tested and training results are used to attain the minimum Mean Square Errors (MSEs). The IFOC is involved in this proposed system to ensure high performance under the variable speed drives. The proposed scheme is validated in both MATLAB/Simulink and experimental platforms.