Rolling element bearings find widespread domestic and industrial application. Defects in bearing unless detected in time may lead to malfunctioning of the machinery. Different methods are used for detection and diagnosis of the bearing defects. This paper is intended as a tutorial overview of bearing vibration signature analysis as a medium for fault detection. An explanation for the causes for the defects is discussed. Vibration measurement in both time domain and frequency domain is presented. Recent trends in research on the detection of the defects in bearings have been included.
In this paper, the mathematical model developed for relationship between viscosity and temperature for the lubricant SAE 15W40 multi grade engine oil with Al 2 O 3 and ZnO nanoparticles is presented. The developed mathematical model for viscosity and temperature of lubricant containing nanoparticles is used for the computation of static performance characteristics of the bearing. These performance characteristics mainly depend on the viscosity of the lubricant. The addition of nanoparticles on commercially available lubricant considerably enhances the viscosity of lubricant and in turn changes the performance characteristics. To obtain pressure and temperature distribution, modified Reynolds and energy equations are used, and these equations are solved by using Finite Element Method. An iterative procedure is used to establish the film extent. The performance characteristics are calculated from the obtained pressure field. The computed results show that addition of nanoparticles increase the viscosity of lubricant and in turn change the performance characteristics of journal bearing.
Cerium oxide (CeO 2 ) nanoparticles are added to different lubricating oils together with suitable surfactants to obtain modified nanolubricants. Lubricating properties of these surfactant modified nanolubricants have been investigated using a pin on disc tribotester under boundary lubrication conditions. Coconut oil, paraffin oil and a commercial engine oil (SAE15W40) have been used as base oils for the present study. Changes in frictional coefficient and specific wear rate have been studied as a function of nanoparticle concentration in the lubricant together with an estimation of the settling trend of nanoparticles with time. Results show that the frictional force and specific wear rate decrease with increase in concentration of nanoparticles, come to a minimum at a specific concentration level and then increase, showing the presence of an optimum concentration level at which both friction and wear are the least. At this concentration level, coconut oil shows the lowest frictional coefficient and specific wear rate among the three oils studied. The morphology of the pin surfaces after sliding has been studied using atomic force microscopy, optical interferometer and SEM. When CeO 2 nanoparticles are added to the lubrication oil together with surfactant modification, settling trend and agglomeration of nanoparticles with time are reduced to a great extent compared to the case in which nanoparticles are added without surfactant modification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.