Motor fault detection using sound signature and wavelet transform

Emad Awada*, Aws Al-Qaisi, Eyad Radwan, Mutasim Nour

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
41 Downloads (Pure)


The use of induction machines has gained fast popularity in many aspects of today’s energy applications and industrial productions. However, just as with any other machine, failure is expected due to a variety of faults in component and system levels. Therefore, it is necessary to improve machine reliability by performing preventive maintenance and exploring faulty indications in advance to avoid future failures. In normal operation, a distinct machine sound signature can be identify. Therefore, at any faulty operation, diagnosis of potential error can be defined based on output signature sound data analysis. Yet, this process of monitoring induction machine sounds and vibration can be hectic and extensive in terms of collecting data and compiling analysis. That is, a huge number of data samples need to be collected and stored in order to define abnormality operation. Therefore, in this work, wavelet-based algorithms were developed as an analysis process to analyze collected data and identify abnormality, with much fewer data samples and compiling process, as special prosperity of wavelet transform. As a result, MATLAB codes were implemented to analyze data based on sound signature technique and wavelet transform algorithms to show a significant improvement in identifying potential error and abnormality conditions.

Original languageEnglish
Pages (from-to)247-255
Number of pages9
JournalInternational Journal of Power Electronics and Drive Systems
Issue number1
Publication statusPublished - Mar 2022


  • Condition monitoring
  • Discrete wavelet transform
  • Fault diagnosis
  • Induction motor
  • Sound analysis

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Motor fault detection using sound signature and wavelet transform'. Together they form a unique fingerprint.

Cite this