MATEC Web Conf.
Volume 184, 2018Annual Session of Scientific Papers IMT ORADEA 2018
|Number of page(s)||6|
|Section||Machines Engineering and Technologies|
|Published online||31 July 2018|
The lubricants' parameters monitoring and data collecting
University of Oradea, Faculty of Manag. and Technological Eng., Industrial Engineering Department, Universitatii 1, Romania
2 Zarqa University, Faculty of Engineering Technology, P.O. Box 132222 Zarqa 13132, Jordan
3 University of Oradea, Faculty of Manag. and Techn. Eng., Mechanical Eng. and Automotive Department, Universitatii 1, Romania
* Corresponding author : email@example.com
This approach is focused on Machine Intelligence for Diagnosis Automation, a research program, which promotes « preventative maintenance in manufacturing plants through the development of a fully automated prototype for oil analysis and fault prediction. The prototype is based on Artificial Intelligence (A.I.) software and online hardware ». Monitoring the condition of lubricants requires the examination of the physical, chemical and additive states, which maintain the quality of the lubricants, which is necessary for the proper functioning of the equipment. A lubricant monitoring program, especially from a qualitative point of view, will need to focus on both machine tool wear and degradation of lubricants, as well as on detecting and describing abnormal working conditions for both lubricants and machine parts. This goal can be satisfied by examining all the oils used in a company by completing laboratory tests to generate steps and acceptance classes, as well as unplanned contingency analyzes. These laboratory tests can be concentrated and classified into technology-based data sheets based on test-based information and test results, ultimately constituting consistent databases needed to generate monitoring and prevention reports. Data on the condition of the oil parameters used in the hydraulic system for lubricating machine tools have been collected during six months. The data as matrix organized, with 258648 rows (observations) and 21 columns (parameters).
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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