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Particle counter and neural network used to detect sliding wear and pitting in a radial hydraulic motor
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
2010 (English)In: International Journal of Fluid Power, ISSN 1439-9776, Vol. 11, no 2, p. 5-13Article in journal (Refereed) Published
Abstract [en]

A particle counter was used to detect sliding wear and pitting in a low-speed hydraulic motor. The features used by a neural network were accumulated mass, number of particles and time above threshold. The diagnostic tool was experimentally evaluated by collecting data from a test rig running under heavy-duty conditions in a laboratory.Accumulated time above a threshold value seems to be an adequate feature to detect severe damage to a low speed motor at constant operating conditions. Using a neural network to combine the three features gives earlier and more reliable detection of which wear mode is prevailing than when only using the features singly.

Place, publisher, year, edition, pages
2010. Vol. 11, no 2, p. 5-13
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-5190Local ID: 33a9a800-020a-11e0-9633-000ea68e967bOAI: oai:DiVA.org:ltu-5190DiVA, id: diva2:978064
Note
Validerad; 2010; 20101207 (ovei)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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Isaksson, Ove

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