Towards Fault Diagnosis in Induction Motor using Fractional Fourier Transform

TL;DR

This paper presents a real-time fault diagnosis method for induction motors using Fractional Fourier Transform, analyzing current signatures to distinguish healthy from faulty motors based on harmonic distortion and error thresholds.

Contribution

Introduces a novel real-time fault detection technique for induction motors utilizing FrFT and relative norm error analysis, enhancing fault diagnosis accuracy.

Findings

Unhealthy motors exhibit higher total harmonic distortion.

Threshold relative norm error for healthy motors is below 0.3.

Unhealthy motors have a relative norm error above 0.5.

Abstract

A method for determining the current signature faults using Fractional Fourier Transform (FrFT) has been developed. The method has been applied to the real-time steady-state current of the inverter-fed high power induction motor for fault determination. The method incorporates calculating the relative norm error to find the threshold value between healthy and unhealthy induction motor at different operating frequencies. The experimental results demonstrate that the total harmonics distortion of unhealthy motor is much larger than the healthy motor, and the threshold relative norm error value of different healthy induction motors is less than 0.3, and the threshold relative norm error value of unhealthy induction motor is greater than 0.5. The developed method can function as a simple operator-assisted tool for determining induction motor faults in real-time.