Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review
Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This pa...
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my.utm.517102018-08-27T03:24:21Z http://eprints.utm.my/id/eprint/51710/ Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review Hassan Ali, Yasir Abdul Rahman, Roslan Raja Hamzah, Raja Ishak TJ Mechanical engineering and machinery Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature Penerbit UTM 2014 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/51710/1/YasirHassanAli2014_Acousticemissionsignalanalysis.pdf Hassan Ali, Yasir and Abdul Rahman, Roslan and Raja Hamzah, Raja Ishak (2014) Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review. Jurnal Teknologi (Sciences and Engineering), 69 (2). pp. 121-126. ISSN 2180-3722 http://dx.doi.org/10.11113/jt.v69.3121 DOI: 10.11113/jt.v69.3121 |
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TJ Mechanical engineering and machinery Hassan Ali, Yasir Abdul Rahman, Roslan Raja Hamzah, Raja Ishak Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
description |
Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature |
format |
Article |
author |
Hassan Ali, Yasir Abdul Rahman, Roslan Raja Hamzah, Raja Ishak |
author_facet |
Hassan Ali, Yasir Abdul Rahman, Roslan Raja Hamzah, Raja Ishak |
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Hassan Ali, Yasir |
title |
Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
title_short |
Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
title_full |
Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
title_fullStr |
Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
title_full_unstemmed |
Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
title_sort |
acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review |
publisher |
Penerbit UTM |
publishDate |
2014 |
url |
http://eprints.utm.my/id/eprint/51710/1/YasirHassanAli2014_Acousticemissionsignalanalysis.pdf http://eprints.utm.my/id/eprint/51710/ http://dx.doi.org/10.11113/jt.v69.3121 |
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13.18916 |