Enhancing Bearing Fault Diagnosis in Induction Motors: A Novel Approach Leveraging Synchronized Deviation of Instantaneous Frequency of Voltage and Current

Current-based methods for bearing fault diagnosis primarily rely on analyzing the current signal, leading to challenges in detecting fault frequencies due to their low magnitude amid the noise in the current spectrum. This issue intensifies for weak bearing faults in their early stages. The presence of noise components increases the risk of false alarms, as fault characteristics are often obscured in the raw current spectral analysis. To address this, effective bearing fault diagnosis necessitates the reduction of noise components. This paper presents a novel noise cancellation method that enhances the estimation of bearing fault signals in induction motors by utilizing the deviation of instantaneous frequency in synchronized motor voltage and current signals. The proposed method efficiently diagnoses bearing fault characteristic frequencies during spectral analysis. Simulation and experimental results substantiate the effectiveness of this approach in detecting outer/inner raceway and ball-bearing faults.