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

Khan, Muhammad Atif; Starr, Andrew; Cooper, Dennis

doi:10.1784/insi.2007.49.12.726

Cited by 5 publications

(1 citation statement)

References 30 publications

(32 reference statements)

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“…Due to its basic monitoring art, i.e. estimating a machine component surface condition, the wear debris analysis has the potential to provide early health diagnostics of machine components as compared to other methodologies [2,3]. But to utilize this early diagnostic potential is one of the prime issues that still need a reply from debris analysis researchers.…”

Section: Introductionmentioning

confidence: 99%

A methodology for online wear debris morphology and composition analysis

Khan

Starr

Cooper

2008

Proceedings of the Institution of Mechanical Engineers, Part J:

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Original article can be found at: http://www.pepublishing.com/ Copyright Professional Engineering Publishing [Full text of this article is not available in the UHRA]Online or inline detection of basic debris features, i.e. size, quantity, size distribution, shape, and compositions simultaneously With real time diagnostics is one of the possible ways to perform Wear debris analysis with high reliability. At present many techniques and sensors are available that can perform near real time detection and diagnostics for debris quantitative features. But to perform real time detection and diagnostics for features like shape and composition still requires a reliable technical concept. In this article a new technique for online wear debris shape and composition analysis is described. The developed technique is a combination of hardware and software based oil imaging technology for shape and composition detection. Rule-based algorithms are used to perform near real time debris analysis diagnostics. An experimental study is also presented that shows the possible potential of the developed technique on real applications

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Section: Introductionmentioning

confidence: 99%