Academic and commercial efforts in Wear Debris Analysis Automation (WDAA)
Academic and commercial domain researchers have introduced many state-of-the-art techniques to perform wear debris analysis. However, due to lack of automation, debris analysis still remains a difficult technique to implement and practise. The research world commenced debris analysis automation...
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my.utp.eprints.40712017-01-19T08:26:50Z Academic and commercial efforts in Wear Debris Analysis Automation (WDAA) Khan, Muhammad Cooper, Dennis Starr, Andrew TJ Mechanical engineering and machinery Academic and commercial domain researchers have introduced many state-of-the-art techniques to perform wear debris analysis. However, due to lack of automation, debris analysis still remains a difficult technique to implement and practise. The research world commenced debris analysis automation efforts in the late 70s, but up until now both academic as well as commercial researchers have not utilised the full potential of wear debris for machine diagnostics. In this regard a comprehensive review is presented here. This review covers the automation efforts made in academic and commercial sectors for the last 28 years. In the review of academic efforts, special emphasis is made regarding techniques such as image processing, artificial intelligence, mathematical tools, statistical analysis tools and sensing methodologies. The review of commercial aspects is explained according to the debris inspection mode in the machine lubricant system. One aspect of debris analysis automation research, which is still missing in current available literature, is also mentioned at the end. 2007-12 Article PeerReviewed application/pdf http://eprints.utp.edu.my/4071/1/Secondjournalpaper.pdf Khan, Muhammad and Cooper, Dennis and Starr, Andrew (2007) Academic and commercial efforts in Wear Debris Analysis Automation (WDAA). Academic and commercial efforts in Wear Debris Analysis Automation (WDAA) . http://eprints.utp.edu.my/4071/ |
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TJ Mechanical engineering and machinery Khan, Muhammad Cooper, Dennis Starr, Andrew Academic and commercial efforts in Wear Debris Analysis Automation (WDAA) |
description |
Academic and commercial domain researchers have
introduced many state-of-the-art techniques to perform wear
debris analysis. However, due to lack of automation, debris
analysis still remains a difficult technique to implement and
practise. The research world commenced debris analysis
automation efforts in the late 70s, but up until now both
academic as well as commercial researchers have not utilised
the full potential of wear debris for machine diagnostics. In
this regard a comprehensive review is presented here. This
review covers the automation efforts made in academic
and commercial sectors for the last 28 years. In the review
of academic efforts, special emphasis is made regarding
techniques such as image processing, artificial intelligence, mathematical tools, statistical analysis tools and sensing methodologies. The review of commercial aspects is explained according to the debris inspection mode in the machine lubricant system. One aspect of debris analysis automation research, which is still missing in current available literature, is also mentioned at the end. |
format |
Article |
author |
Khan, Muhammad Cooper, Dennis Starr, Andrew |
author_facet |
Khan, Muhammad Cooper, Dennis Starr, Andrew |
author_sort |
Khan, Muhammad |
title |
Academic and commercial efforts in Wear Debris
Analysis Automation (WDAA) |
title_short |
Academic and commercial efforts in Wear Debris
Analysis Automation (WDAA) |
title_full |
Academic and commercial efforts in Wear Debris
Analysis Automation (WDAA) |
title_fullStr |
Academic and commercial efforts in Wear Debris
Analysis Automation (WDAA) |
title_full_unstemmed |
Academic and commercial efforts in Wear Debris
Analysis Automation (WDAA) |
title_sort |
academic and commercial efforts in wear debris
analysis automation (wdaa) |
publishDate |
2007 |
url |
http://eprints.utp.edu.my/4071/1/Secondjournalpaper.pdf http://eprints.utp.edu.my/4071/ |
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1738655319697915904 |