Artificial neural network for the classification of steel hollow pipe
Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
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Universiti Malaysia Perlis
2009
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| Online Access: | http://dspace.unimap.edu.my/xmlui/handle/123456789/7230 |
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my.unimap-72302009-11-17T06:26:04Z Artificial neural network for the classification of steel hollow pipe Nur Farahiyah, Mohamad Hafizawati, Zakaria Rakhmad Arief, Siregar Hariharan, M. Fauziah, Mat Artificial intelligence Neural networks (Computer science) Pipe, Steel Nondestructive testing Pipelines Crack detection Tubes, Steel -- Cracking Pipe, Steel -- Cracking Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. Within industry, piping is a very important system that used to convey fluid (liquid and gases) from one location to another. Steel pipe is one of the commonly type of pipe that has been used since before. Crack on pipe is one of the things that always happen on pipe due to transfer fluid. Non-destructive (NDT) testing is responsible to detect the damage on pipe to avoid from bursting. From this, the paper presents a NDT method to detect damage in pipe by using Artificial Intelligence Neural Network (ANN) to compare Frequency Response Function (FRF) derived from impact testing on intact and damage pipe. Carbon Steel pipe with different hollow through the pipe in free-free condition is considered as a specimen. A simple feedforward with multilayer backpropagation neural network models is developed for the recognition of intact and damage steel pipe. FRF data presented on variation of amplitude load vs. frequency wave depends on disposition features can be very useful in crack detection in pipelines knowing the frequencies. This indicates that the representation of intact and damage pipe by the frequency using Artificial Neural Network (ANN) is reasonably accurate. Experimental results demonstrate that the recognition rate of the proposed neural network models is about 91.48% 2009-11-09T07:03:22Z 2009-11-09T07:03:22Z 2009-10-11 Working Paper p.9B 1 - 9B 4 978-967-5415-07-4 http://hdl.handle.net/123456789/7230 en Proceedings of International Conference on Applications and Design in Mechanical Engineering 2009 (iCADME 2009) Universiti Malaysia Perlis |
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Malaysia |
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Universiti Malaysia Perlis |
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| language |
English |
| topic |
Artificial intelligence Neural networks (Computer science) Pipe, Steel Nondestructive testing Pipelines Crack detection Tubes, Steel -- Cracking Pipe, Steel -- Cracking |
| spellingShingle |
Artificial intelligence Neural networks (Computer science) Pipe, Steel Nondestructive testing Pipelines Crack detection Tubes, Steel -- Cracking Pipe, Steel -- Cracking Nur Farahiyah, Mohamad Hafizawati, Zakaria Rakhmad Arief, Siregar Hariharan, M. Fauziah, Mat Artificial neural network for the classification of steel hollow pipe |
| description |
Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. |
| format |
Working Paper |
| author |
Nur Farahiyah, Mohamad Hafizawati, Zakaria Rakhmad Arief, Siregar Hariharan, M. Fauziah, Mat |
| author_facet |
Nur Farahiyah, Mohamad Hafizawati, Zakaria Rakhmad Arief, Siregar Hariharan, M. Fauziah, Mat |
| author_sort |
Nur Farahiyah, Mohamad |
| title |
Artificial neural network for the classification of steel hollow pipe |
| title_short |
Artificial neural network for the classification of steel hollow pipe |
| title_full |
Artificial neural network for the classification of steel hollow pipe |
| title_fullStr |
Artificial neural network for the classification of steel hollow pipe |
| title_full_unstemmed |
Artificial neural network for the classification of steel hollow pipe |
| title_sort |
artificial neural network for the classification of steel hollow pipe |
| publisher |
Universiti Malaysia Perlis |
| publishDate |
2009 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/7230 |
| _version_ |
1643788762689306624 |