Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine – A Review
Machinery condition monitoring has become one of the essential components in the industry due to the ability of providing insight to the machine condition during operation as well as enhancing productivity and increasing machine reliability. This paper provides a review on using acoustic emission...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Universiti Malaysia Pahang
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/15909/ http://umpir.ump.edu.my/id/eprint/15909/ http://umpir.ump.edu.my/id/eprint/15909/1/P032%20pg212-219.pdf |
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ump-15909 |
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eprints |
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ump-159092017-03-29T01:03:04Z http://umpir.ump.edu.my/id/eprint/15909/ Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine – A Review Yasir , Hassan Ali Salah, M. Ali Roslan, Abd Rahman Raja Ishak, Raja Hamzah TJ Mechanical engineering and machinery Machinery condition monitoring has become one of the essential components in the industry due to the ability of providing insight to the machine condition during operation as well as enhancing productivity and increasing machine reliability. This paper provides a review on using acoustic emission (AE) technique combined with artificial intelligence (AI) methods in the field of machinery condition monitoring and fault diagnosis. Even though many papers have been published in the area of machinery condition monitoring, this paper puts emphasis on gears and bearing only. Furthermore, the paper attempts to summarize and evaluate the recent condition monitoring research that utilizing AI includes fuzzy logic, artificial neural network (ANN), support vector machine (SVM), and genetic algorithms (GA) in fault diagnosis, fault classification, fault localization and fault size estimation in gear and bearing based on features extraction from AE signal. Machine condition monitoring philosophy and techniques have evolved based on intellectual systems. However, the acquired AE signal was found to be complicated in the application of gear and bearing monitoring, therefore it is required more attention. In addition, the use of AI methods in gear and bearing fault diagnosis still in the growing stage that requires lots of encouragement as it has a promising future in the field of machinery condition monitoring. Universiti Malaysia Pahang 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/15909/1/P032%20pg212-219.pdf Yasir , Hassan Ali and Salah, M. Ali and Roslan, Abd Rahman and Raja Ishak, Raja Hamzah (2016) Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine – A Review. In: National Conference For Postgraduate Research (NCON-PGR 2016), 24-25 September 2016 , Universiti Malaysia Pahang, Pekan. pp. 212-219.. http://ee.ump.edu.my/ncon/wp-content/uploads/2016/10/Proceeding-NCON-PGR-2016.zip |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
TJ Mechanical engineering and machinery |
| spellingShingle |
TJ Mechanical engineering and machinery Yasir , Hassan Ali Salah, M. Ali Roslan, Abd Rahman Raja Ishak, Raja Hamzah Acoustic Emission and Artificial Intelligent Methods in Condition Monitoring of Rotating Machine – A Review |
| description |
Machinery condition monitoring has become one of the essential components in the industry due to the ability of
providing insight to the machine condition during operation as well as enhancing productivity and increasing machine
reliability. This paper provides a review on using acoustic emission (AE) technique combined with artificial intelligence (AI)
methods in the field of machinery condition monitoring and fault diagnosis. Even though many papers have been published in
the area of machinery condition monitoring, this paper puts emphasis on gears and bearing only. Furthermore, the paper attempts
to summarize and evaluate the recent condition monitoring research that utilizing AI includes fuzzy logic, artificial neural
network (ANN), support vector machine (SVM), and genetic algorithms (GA) in fault diagnosis, fault classification, fault
localization and fault size estimation in gear and bearing based on features extraction from AE signal. Machine condition
monitoring philosophy and techniques have evolved based on intellectual systems. However, the acquired AE signal was found
to be complicated in the application of gear and bearing monitoring, therefore it is required more attention. In addition, the use
of AI methods in gear and bearing fault diagnosis still in the growing stage that requires lots of encouragement as it has a
promising future in the field of machinery condition monitoring. |
| format |
Conference or Workshop Item |
| author |
Yasir , Hassan Ali Salah, M. Ali Roslan, Abd Rahman Raja Ishak, Raja Hamzah |
| author_facet |
Yasir , Hassan Ali Salah, M. Ali Roslan, Abd Rahman Raja Ishak, Raja Hamzah |
| author_sort |
Yasir , Hassan Ali |
| title |
Acoustic Emission and Artificial Intelligent
Methods in Condition Monitoring of Rotating
Machine – A Review |
| title_short |
Acoustic Emission and Artificial Intelligent
Methods in Condition Monitoring of Rotating
Machine – A Review |
| title_full |
Acoustic Emission and Artificial Intelligent
Methods in Condition Monitoring of Rotating
Machine – A Review |
| title_fullStr |
Acoustic Emission and Artificial Intelligent
Methods in Condition Monitoring of Rotating
Machine – A Review |
| title_full_unstemmed |
Acoustic Emission and Artificial Intelligent
Methods in Condition Monitoring of Rotating
Machine – A Review |
| title_sort |
acoustic emission and artificial intelligent
methods in condition monitoring of rotating
machine – a review |
| publisher |
Universiti Malaysia Pahang |
| publishDate |
2016 |
| url |
http://umpir.ump.edu.my/id/eprint/15909/ http://umpir.ump.edu.my/id/eprint/15909/ http://umpir.ump.edu.my/id/eprint/15909/1/P032%20pg212-219.pdf |
| first_indexed |
2023-09-18T22:21:06Z |
| last_indexed |
2023-09-18T22:21:06Z |
| _version_ |
1777415662244724736 |