2000
DOI: 10.1108/00368790010326410
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Assessment of useful life of lubricants using artificial neural network

Abstract: Most catastrophic failures of machines result from improper lubrication. The cost of lubricants can be up to 10‐12 percent of the production cost an essential part of maintenance management, therefore it is essential that the optimization of lubricants is at the optimum. This paper decribes and considers present practice in lubricant condition monitoring.

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Cited by 12 publications
(8 citation statements)
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“…An important characteristic of a lubricant is the ''Remaining Useful Life'', which represents the remaining time, in which the lubricant becomes unfit for the operation in continuous use. However, the lubricant degradation pattern also depends on the characteristics of the machine, in which the lubricant is applied, see [32] and the references therein. For robotic applications, preventive, scheduled maintenance can be performed to verify the quality of the lubricant in actuators and transmissions [5].…”
Section: Introductionmentioning
confidence: 99%
“…An important characteristic of a lubricant is the ''Remaining Useful Life'', which represents the remaining time, in which the lubricant becomes unfit for the operation in continuous use. However, the lubricant degradation pattern also depends on the characteristics of the machine, in which the lubricant is applied, see [32] and the references therein. For robotic applications, preventive, scheduled maintenance can be performed to verify the quality of the lubricant in actuators and transmissions [5].…”
Section: Introductionmentioning
confidence: 99%
“…However, the lubricant degradation pattern also depends on the characteristics of the machine in which the lubricant is applied, see [19] and the references therein.…”
Section: Lubricant Quality Monitoring In Gear Transmissionsmentioning
confidence: 99%
“…• Based on the available measurements in the time interval (t d , t) and with known fault pattern functions, calculate the elements (discrete time fault pattern signals) of the set F, defined in (19). • Calculate the elements of the (filtered) fault power pattern signal set P f , defined in (21), by solving the discretized form of the linear differential (22) for each element of F. • Determine the correlation coefficient array C f , given by (23).…”
Section: Fault Isolation and Identificationmentioning
confidence: 99%
“…The work was supplemented by a case study on heavy earth moving machinery deployed in Indian mines. Sinha et al (2000) using neural network assessed the useful life of lubricants. The time required for a fresh lubricant to become unfit for use is known as useful life of lubricant.…”
Section: Introductionmentioning
confidence: 99%