Bibliographic citations
Yon, C., (2016). Evaluación y metodología de criterios de mantenimiento predictivo para el mejoramiento del desempeño de lubricantes industriales en procesos industriales [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/5817
Yon, C., Evaluación y metodología de criterios de mantenimiento predictivo para el mejoramiento del desempeño de lubricantes industriales en procesos industriales [Tesis]. : Universidad Nacional de Ingeniería; 2016. http://hdl.handle.net/20.500.14076/5817
@misc{renati/707923,
title = "Evaluación y metodología de criterios de mantenimiento predictivo para el mejoramiento del desempeño de lubricantes industriales en procesos industriales",
author = "Yon Olivos, César Manuel",
publisher = "Universidad Nacional de Ingeniería",
year = "2016"
}
The following research work was carried out in order to design and develop a model predictive maintenance in industrial lubricants based on condition focused on improving the effectiveness of rotary assets critical industrial processes to reduce the hours of stop unscheduled raise productivity and minimize system costs associated with maintenance. To accomplish this, first a predictive diagnosis of lubricants and a collection of technical information were performed. Then, critical equipment is determined and underwent Modes and Effects Analysis fault for any industrial process equipment to determine recurring failure modes and their consequences. This analysis was the basis for selecting the predictive tools, and from these inspection procedures and other key aspects such as measurement points, sampling routes, frequency of inspection activities were determined. Once designed all the predictive model in lubricants, resulting in the detection of various problems that, if not corrected in time, can cause inspective flaws that would impact significantly on the production, safety and environment, largely non-programming and implementation of predictive maintenance actions, recommended analysis reports after each inspection lubricants generated. Predictive maintenance based on condition is to perform systematic measurements of operational variables of machinery and industrial equipment. By monitoring and recording, through regular checks, key parameters on the performance of a machine, and operational variables, state lubricants, time between failures, it is possible to obtain patterns or signals that, when analyzed, can determine the condition of the equipment, and This way you can plan maintenance activities and schedule specific time to active intervention before failures pose a risk to personal safety time, the environment, the integrity of the equipment and thus guarantee the continuity of the production process. The paper begins by describing the important aspects of tribology, the science of friction, wear and lubrication. We continue with the comprehensive study of lubricating oils: Origin, classification, components, functions performed by physical and chemical parameters that describe and differentiate, and how they change and deteriorate their characteristics during their lifetime helpful. Clearly it determined that lubricating oils accumulate in themselves valuable information on the state of the mechanical components and equipment in which they work. It was determined that a key tool used by the predictive maintenance is the laboratory analysis of lubricating oils during and at the end of its useful life. The proper interpretation of the information supplied oils to diagnose the state of the equipment and its components in order to schedule appropriate maintenance actions for safekeeping. Then we started with a study to determine the demand laboratories lubricants in the country, showed that there is room for a laboratory bid with highly technical analysis service of lubricating oils, a description of standardized methods are also included ISO and ASTM for handling samples and measuring parameters. Also they described steps to be followed to certify the standards laboratory under ISO / IEC 17025 as the investment required, and the assessment and management of environmental risks is finally explained.
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