Rotor Bar Fault Diagnosis in Indirect Field–Oriented Control-Fed Induction Motor Drive Using Hilbert Transform, Discrete Wavelet Transform, and Energy Eigenvalue Computation

Abstract

The most powerful technology in the condition-based maintenance (CBM) framework for rotating machinery is fault detection (FD) and fault diagnosis (FDS). This paper investigates the broken rotor bar (BRB) FDS utilizing Hilbert transform (HT), discrete wavelet transform (DWT), and energy eigenvalue (EEV) computation with the induction motor (IM) drive handled by the indirect field orientation control (IFOC). The stator current spectrum, which the HT collects, is utilized to determine BRB degradation. The DWT decomposes the signal while the fast Fourier transform (FFT) recovers the signal’s frequency and amplitude factors. The EEV of the motor current in the signal determines the degree of the malfunction and provides a better method for recognizing errors. The DWT is used to overcome the Fourier analysis’s drawbacks and is primarily dedicated to non-stationary signals. While DWT is used, the malfunctioning BRB’s stator current signal is restrained from its original amplitude. The results demonstrate that the proposed method can identify and diagnose faults in an IM drive even under different loads.