Acoustic Based Fault Analysis of Induction Motor Using FFT and Wavelets- A Comparative Study

Detection of fault in induction motors is indispensable for avoiding unforeseen failures. This work intends to diagnose bearing faultin an induction motor. Fault diagnosis is performed on the basis of audio signals emitted by the rotor during rotation of an induction motor. The audio signals which are obtained via a microphone is analysed in Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) methods. It is seen that even though FFT technique was able to differentiate between the frequency spectrum of the faulty motor and the healthy motor, discrete wavelet transform showed a better level of differentiation with respect to the faulty condition and the healthy condition, which were evaluated based on certain statistical parameters.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-03-27
DOI
https://doi.org/10.52783/jisem.v10i25s.4098
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Acoustic Based Fault Analysis of Induction Motor Using FFT and Wavelets- A Comparative Study

Keerthana Unni
Journal of Information Systems Engineering & Management
Machine Fault Diagnosis Techniques
article

Acoustic Based Fault Analysis of Induction Motor Using FFT and Wavelets- A Comparative Study

Keerthana Unni
article en

Abstract

Detection of fault in induction motors is indispensable for avoiding unforeseen failures. This work intends to diagnose bearing faultin an induction motor. Fault diagnosis is performed on the basis of audio signals emitted by the rotor during rotation of an induction motor. The audio signals which are obtained via a microphone is analysed in Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) methods. It is seen that even though FFT technique was able to differentiate between the frequency spectrum of the faulty motor and the healthy motor, discrete wavelet transform showed a better level of differentiation with respect to the faulty condition and the healthy condition, which were evaluated based on certain statistical parameters.

Journal of Information Systems Engineering & ManagementVol. 10(25s)
Openalex Percentile: Top 93%
Machine Fault Diagnosis Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.