Browsing by Author "Granja, Victoria"

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    Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas
    (MDPI, 2021) Desai, Prathamesh S.; Granja, Victoria; Higgs, C. Fred
    The recent decline in crude oil prices due to global competition and COVID-19-related demand issues has highlighted the need for the efficient operation of an oil and gas plant. One such avenue is accurate predictions about the remaining useful life (RUL) of components used in oil and gas plants. A tribosystem is comprised of the surfaces in relative motion and the lubricant between them. Lubricant oils play a significant role in keeping any tribosystem such as bearings and gears working smoothly over the lifetime of the oil and gas plant. The lubricant oil needs replenishment from time to time to avoid component breakdown due to the increased presence of wear debris and friction between the sliding surfaces of bearings and gears. Traditionally, this oil change is carried out at pre-determined times. This paper explored the possibilities of employing machine learning to predict early failure behavior in sensor-instrumented tribosystems. Specifically, deep learning and tribological data obtained from sensors deployed on the components can provide more accurate predictions about the RUL of the tribosystem. This automated maintenance can improve the overall efficiency of the component. The present study aimed to develop a deep learning-based digital twin for accurately predicting the RUL of a tribosystem comprised of a ball bearing-like test apparatus, a four-ball tester, and lubricant oil. A commercial lubricant used in the offshore oil and gas components was tested for its extreme pressure performance, and its welding load was measured using a four-ball tester. Three accelerated deterioration tests was carried out on the four-ball tester at a load below the welding load. Based on the wear scar measurements obtained from the experimental tests, the RUL data were used to train a multivariate convolutional neural network (CNN). The training accuracy of the model was above 99%, and the testing accuracy was above 95%. This work involved the model-free learning prediction of the remaining useful lifetime of ball bearing-type contacts as a function of key sensor input data (i.e., load, friction, temperature). This model can be deployed for in-field tribological machine elements to trigger automated maintenance without explicitly measuring the wear phenomenon.
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    Tribological Properties of h-BN, Ag and MgO Nanostructures as Lubricant Additives in Vegetable Oils
    (MDPI, 2024) Granja, Victoria; Jogesh, Kollol; Taha-Tijerina, Jaime; Higgs, C. Fred; Particle Flow and Tribology Laboratory
    There exists an ever-growing need for sustainable engineering solutions to improve emission control and the energy efficiency of tribosystems. This study examines the tribological properties of two environmentally friendly vegetable fluids, soybean and sunflower oil, with the addition of three non-toxic nanostructures (h-BN, silver and MgO) at different concentrations. It was found that nanostructures added to vegetable oils at specific concentrations can exhibit good friction reduction and wear preventive properties. The addition of h-BN nanosheets in sunflower oil decreased the coefficient of friction and the wear damage, as measured by the wear scar diameter. Silver and magnesium oxide nanoparticles further reduced the friction and wear, respectively. In addition to the tribological testing of the samples, investigations were performed using an optical microscope, SEM and EDX to elucidate the mechanisms that may have led to the observed friction reduction and wear-preventive properties of different nanostructure additives. The thermophysical properties of the samples were also measured. It was found that the thermal conductivity of both base oils could be enhanced by 24% when using h-BN at 0.25 wt% concentration.