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Academic and commercial efforts in Wear Debris Analysis Automation (WDAA)

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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.
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Keywords: Wear debris analysis and automation; artificial intelligence; image processing; inline detection of wear debris; mathematical and statistical tools; online detection of wear debris

Document Type: Research Article

Affiliations: 1 Muhammad Ali Khan received a Bachelors Degree in Mechanical Engineering in 2002. Since then he has worked as a research engineer for over three years in the research area of machine health diagnostics and prognostics. He is currently pursuing his PhD from the University of Manchester. His principal area of research is wear debris analysis automation and healthy machine prognostics.

Publication date: December 1, 2007

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