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Motor fault detection using sound signature and wavelet transform

International Journal of Power Electronics and Drive Systems · Vol. 13 No. 1 (2022) · hlm. 247–255

DOI
10.11591/ijpeds.v13.i1.pp247-255
ISSN
2722-256X, 2088-8694
Terbit
2022-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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20 penulis · 44 tautan kepenulisan bersama · 15 lintas organisasi

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  • Applied Science Private University
  • Al-Balqa Applied University
  • Heriot-Watt University
  • Ohio Northern University
  • Al-Ahliyya Amman University
  • Al Ain University
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Rekan penulisOrganisasiArtikel bersama
Ali M. BaniyounesApplied Science Private University3
Khalid Al-OlimatOhio Northern University3
Ali BaniyounesAfiliasi tidak diketahui3
Khalid S. Al OlimatAfiliasi tidak diketahui3
Emad A. AwadaApplied Science Private University2
Khalil SalihApplied Science Private University2
Saleh A. KhawatrehAl-Ahliyya Amman University1
Ahmad A. SharadqahAl-Balqa Applied University1
Ziad AlqadiAl-Balqa Applied University1
Adnan ManasrehAfiliasi tidak diketahui1
Ahmed A. M. SharadqhAl-Balqa Applied University1
Jawdat S. AlkasassbehAl-Balqa Applied University1
Yazeed Yasin GhadiAl Ain University1
Abdelrahman RadwanApplied Science Private University1
Nasser AbdellatifApplied Science Private University1
Maryam AkhozahiehApplied Science Private University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Motor fault detection using sound signature and wavelet transform</dc:title>
	<dc:creator>Awada, Emad</dc:creator>
	<dc:creator>Al-Qaisi, Aws</dc:creator>
	<dc:creator>Radwan, Eyad</dc:creator>
	<dc:creator>Nour, Mutasim</dc:creator>
	<dc:subject xml:lang="en-US">condition monitoring; discrete wavelet transform;  fault diagnosis; induction motor; sound analysis</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21664</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v13.i1.pp247-255</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 13, No 1: March 2022; 247-255</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21664/13704</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Emad Awada, Aws Al-Qaisi, Eyad Radwan, Mutasim Nour</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>