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Improving hydraulic excavator performance through in line hydraulic oil contamination monitoring

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posted on 30.06.2016, 10:10 by Felix Ng, Jennifer HardingJennifer Harding, Jacqui Glass
It is common for original equipment manufacturers (OEMs) of high value products to provide maintenance or service packages to customers to ensure their products are maintained at peak efficiency throughout their life. To quickly and efficiently plan for maintenance requirements, OEMs require accurate information about the use and wear of their products. In recent decades, the aerospace industry in particular has become expert in using real time data for the purpose of product monitoring and maintenance scheduling. Significant quantities of real time usage data from product monitoring are commonly generated and transmitted back to the OEMs, where diagnostic and prognostic analysis will be carried out. More recently, other industries such as construction and automotive, are also starting to develop capabilities in these areas and condition based maintenance (CBM) is increasing in popularity as a means of satisfying customers’ demands. CBM requires constant monitoring of real time product data by the OEMs, however the biggest challenge for these industries, in particular construction, is the lack of accurate and real time understanding of how their products are being used possibly because of the complex supply chains which exist in construction projects. This research focuses on current dynamic data acquisition techniques for mobile hydraulic systems, in this case the use of a mobile inline particle contamination sensor; the aim was to assess suitability to achieve both diagnostic and prognostic requirements of Condition Based Maintenance. It concludes that hydraulic oil contamination analysis, namely detection of metallic particulates, offers a reliable way to measure real time wear of hydraulic components.


This research was undertaken as part of an EngD project funded by Centre for Innovative and Collaborative Construction Engineering at Loughborough University and a leading company in the off highway industry. The support of the Engineering and Physical Sciences Research Council is gratefully acknowledged (ESPRC Grant EP/G037272/1).



  • Mechanical, Electrical and Manufacturing Engineering

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Mechanical Systems and Signal Processing






NG, F., HARDING, J.A. and GLASS, J., 2016. Improving hydraulic excavator performance through in line hydraulic oil contamination monitoring. Mechanical Systems and Signal Processing, 83, pp. 176-193.


Elsevier © the authors


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This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

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This is an open access article published by Elsevier and distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/